diff --git a/Plots/experiment2_accOct-04-2023,11:21.jpeg b/Plots/experiment2_accOct-04-2023,11:21.jpeg
deleted file mode 100644
index 22d8f02..0000000
Binary files a/Plots/experiment2_accOct-04-2023,11:21.jpeg and /dev/null differ
diff --git a/Plots/experiment2_accOct-04-2023,11:35.jpeg b/Plots/experiment2_accOct-04-2023,11:35.jpeg
deleted file mode 100644
index 22d8f02..0000000
Binary files a/Plots/experiment2_accOct-04-2023,11:35.jpeg and /dev/null differ
diff --git a/Plots/experiment2_accOct-04-2023,12:46.jpeg b/Plots/experiment2_accOct-04-2023,12:46.jpeg
deleted file mode 100644
index 22d8f02..0000000
Binary files a/Plots/experiment2_accOct-04-2023,12:46.jpeg and /dev/null differ
diff --git a/Plots/experiment2_accOct-07-2023,06:04.jpeg b/Plots/experiment2_accOct-07-2023,06:04.jpeg
deleted file mode 100644
index 22d8f02..0000000
Binary files a/Plots/experiment2_accOct-07-2023,06:04.jpeg and /dev/null differ
diff --git a/Plots/experiment2_accOct-08-2023,16:33.jpeg b/Plots/experiment2_accOct-08-2023,16:33.jpeg
deleted file mode 100644
index 22d8f02..0000000
Binary files a/Plots/experiment2_accOct-08-2023,16:33.jpeg and /dev/null differ
diff --git a/Plots/experiment2_accOct-09-2023,06:47.jpeg b/Plots/experiment2_accOct-09-2023,06:47.jpeg
deleted file mode 100644
index 22d8f02..0000000
Binary files a/Plots/experiment2_accOct-09-2023,06:47.jpeg and /dev/null differ
diff --git a/Plots/experiment2_accOct-09-2023,20:51.jpeg b/Plots/experiment2_accOct-09-2023,20:51.jpeg
deleted file mode 100644
index 22d8f02..0000000
Binary files a/Plots/experiment2_accOct-09-2023,20:51.jpeg and /dev/null differ
diff --git a/Plots/experiment2_accOct-15-2023,20:49.jpeg b/Plots/experiment2_accOct-15-2023,20:49.jpeg
new file mode 100644
index 0000000..54fa25d
Binary files /dev/null and b/Plots/experiment2_accOct-15-2023,20:49.jpeg differ
diff --git a/Plots/experiment2_accOct-16-2023,13:05.jpeg b/Plots/experiment2_accOct-16-2023,13:05.jpeg
new file mode 100644
index 0000000..94d0f51
Binary files /dev/null and b/Plots/experiment2_accOct-16-2023,13:05.jpeg differ
diff --git a/Plots/experiment2_accOct-16-2023,13:35.jpeg b/Plots/experiment2_accOct-16-2023,13:35.jpeg
new file mode 100644
index 0000000..95729cb
Binary files /dev/null and b/Plots/experiment2_accOct-16-2023,13:35.jpeg differ
diff --git a/Plots/experiment2_accOct-16-2023,13:59.jpeg b/Plots/experiment2_accOct-16-2023,13:59.jpeg
new file mode 100644
index 0000000..92eb752
Binary files /dev/null and b/Plots/experiment2_accOct-16-2023,13:59.jpeg differ
diff --git a/Plots/experiment2_accOct-04-2023,11:08.jpeg b/Plots/experiment2_accOct-16-2023,14:13.jpeg
similarity index 51%
rename from Plots/experiment2_accOct-04-2023,11:08.jpeg
rename to Plots/experiment2_accOct-16-2023,14:13.jpeg
index 22d8f02..b7c4612 100644
Binary files a/Plots/experiment2_accOct-04-2023,11:08.jpeg and b/Plots/experiment2_accOct-16-2023,14:13.jpeg differ
diff --git a/Plots/experiment2_accOct-16-2023,14:14.jpeg b/Plots/experiment2_accOct-16-2023,14:14.jpeg
new file mode 100644
index 0000000..22c579c
Binary files /dev/null and b/Plots/experiment2_accOct-16-2023,14:14.jpeg differ
diff --git a/Plots/experiment2_accOct-16-2023,14:26.jpeg b/Plots/experiment2_accOct-16-2023,14:26.jpeg
new file mode 100644
index 0000000..dcab216
Binary files /dev/null and b/Plots/experiment2_accOct-16-2023,14:26.jpeg differ
diff --git a/Plots/experiment2_precisionOct-04-2023,11:21.jpeg b/Plots/experiment2_accOct-16-2023,14:47.jpeg
similarity index 53%
rename from Plots/experiment2_precisionOct-04-2023,11:21.jpeg
rename to Plots/experiment2_accOct-16-2023,14:47.jpeg
index 62300a4..552ded6 100644
Binary files a/Plots/experiment2_precisionOct-04-2023,11:21.jpeg and b/Plots/experiment2_accOct-16-2023,14:47.jpeg differ
diff --git a/Plots/experiment2_precisionOct-07-2023,06:05.jpeg b/Plots/experiment2_precisionOct-07-2023,06:05.jpeg
deleted file mode 100644
index 62300a4..0000000
Binary files a/Plots/experiment2_precisionOct-07-2023,06:05.jpeg and /dev/null differ
diff --git a/Plots/experiment2_precisionOct-08-2023,16:33.jpeg b/Plots/experiment2_precisionOct-08-2023,16:33.jpeg
deleted file mode 100644
index 62300a4..0000000
Binary files a/Plots/experiment2_precisionOct-08-2023,16:33.jpeg and /dev/null differ
diff --git a/Plots/experiment2_precisionOct-09-2023,06:47.jpeg b/Plots/experiment2_precisionOct-09-2023,06:47.jpeg
deleted file mode 100644
index 62300a4..0000000
Binary files a/Plots/experiment2_precisionOct-09-2023,06:47.jpeg and /dev/null differ
diff --git a/Plots/experiment2_precisionOct-09-2023,20:51.jpeg b/Plots/experiment2_precisionOct-09-2023,20:51.jpeg
deleted file mode 100644
index 62300a4..0000000
Binary files a/Plots/experiment2_precisionOct-09-2023,20:51.jpeg and /dev/null differ
diff --git a/Plots/experiment2_precisionOct-15-2023,20:49.jpeg b/Plots/experiment2_precisionOct-15-2023,20:49.jpeg
new file mode 100644
index 0000000..255719f
Binary files /dev/null and b/Plots/experiment2_precisionOct-15-2023,20:49.jpeg differ
diff --git a/Plots/experiment2_precisionOct-16-2023,13:05.jpeg b/Plots/experiment2_precisionOct-16-2023,13:05.jpeg
new file mode 100644
index 0000000..7991506
Binary files /dev/null and b/Plots/experiment2_precisionOct-16-2023,13:05.jpeg differ
diff --git a/Plots/experiment2_precisionOct-16-2023,13:35.jpeg b/Plots/experiment2_precisionOct-16-2023,13:35.jpeg
new file mode 100644
index 0000000..d80937c
Binary files /dev/null and b/Plots/experiment2_precisionOct-16-2023,13:35.jpeg differ
diff --git a/Plots/experiment2_precisionOct-16-2023,13:59.jpeg b/Plots/experiment2_precisionOct-16-2023,13:59.jpeg
new file mode 100644
index 0000000..8e57fc2
Binary files /dev/null and b/Plots/experiment2_precisionOct-16-2023,13:59.jpeg differ
diff --git a/Plots/experiment2_precisionOct-04-2023,12:46.jpeg b/Plots/experiment2_precisionOct-16-2023,14:14.jpeg
similarity index 52%
rename from Plots/experiment2_precisionOct-04-2023,12:46.jpeg
rename to Plots/experiment2_precisionOct-16-2023,14:14.jpeg
index 62300a4..ff43875 100644
Binary files a/Plots/experiment2_precisionOct-04-2023,12:46.jpeg and b/Plots/experiment2_precisionOct-16-2023,14:14.jpeg differ
diff --git a/Plots/experiment2_precisionOct-04-2023,11:35.jpeg b/Plots/experiment2_precisionOct-16-2023,14:26.jpeg
similarity index 52%
rename from Plots/experiment2_precisionOct-04-2023,11:35.jpeg
rename to Plots/experiment2_precisionOct-16-2023,14:26.jpeg
index 62300a4..44decbf 100644
Binary files a/Plots/experiment2_precisionOct-04-2023,11:35.jpeg and b/Plots/experiment2_precisionOct-16-2023,14:26.jpeg differ
diff --git a/Plots/experiment2_precisionOct-04-2023,11:08.jpeg b/Plots/experiment2_precisionOct-16-2023,14:47.jpeg
similarity index 57%
rename from Plots/experiment2_precisionOct-04-2023,11:08.jpeg
rename to Plots/experiment2_precisionOct-16-2023,14:47.jpeg
index 62300a4..a1709af 100644
Binary files a/Plots/experiment2_precisionOct-04-2023,11:08.jpeg and b/Plots/experiment2_precisionOct-16-2023,14:47.jpeg differ
diff --git "a/Table/config\\_exp2 central attack.tex" "b/Table/config\\_exp2 central attack.tex"
new file mode 100644
index 0000000..f84f000
--- /dev/null
+++ "b/Table/config\\_exp2 central attack.tex"	
@@ -0,0 +1,19 @@
+\begin{table}[h]
+\centering
+\begin{tabular}{|c|c|}
+\hline
+parameter & value\\
+\hline
+max_depth & 12 \\\hline
+objective & binary:logistic \\\hline
+tree_method & approx \\\hline
+learning_rate & 0.3 \\\hline
+n_estimators & 20 \\\hline
+gamma & 0 \\\hline
+reg_alpha & 0.0 \\\hline
+reg_lambda & 1 \\\hline
+model & XGBClassifier \\\hline
+
+\end{tabular}\caption{./Table/config_exp2 central attack}
+\label{tab:./Table/config_exp2 central attack}
+\end{table}
diff --git "a/Table/config\\_exp2 federated attack1.tex" "b/Table/config\\_exp2 federated attack1.tex"
new file mode 100644
index 0000000..6cebbf8
--- /dev/null
+++ "b/Table/config\\_exp2 federated attack1.tex"	
@@ -0,0 +1,19 @@
+\begin{table}[h]
+\centering
+\begin{tabular}{|c|c|}
+\hline
+parameter & value\\
+\hline
+max_depth & 12 \\\hline
+objective & binary:logistic \\\hline
+tree_method & exact \\\hline
+learning_rate & 0.3 \\\hline
+n_estimators & 40 \\\hline
+gamma & 0 \\\hline
+reg_alpha & 0 \\\hline
+reg_lambda & 1 \\\hline
+model & XGBClassifier \\\hline
+
+\end{tabular}\caption{./Table/config_exp2 federated attack1}
+\label{tab:./Table/config_exp2 federated attack1}
+\end{table}
diff --git "a/Table/config\\_exp2 federated attack2.tex" "b/Table/config\\_exp2 federated attack2.tex"
new file mode 100644
index 0000000..e64f097
--- /dev/null
+++ "b/Table/config\\_exp2 federated attack2.tex"	
@@ -0,0 +1,19 @@
+\begin{table}[h]
+\centering
+\begin{tabular}{|c|c|}
+\hline
+parameter & value\\
+\hline
+max_depth & 12 \\\hline
+objective & binary:logistic \\\hline
+tree_method & exact \\\hline
+learning_rate & 0.3 \\\hline
+n_estimators & 20 \\\hline
+gamma & 0 \\\hline
+reg_alpha & 0 \\\hline
+reg_lambda & 1 \\\hline
+model & XGBClassifier \\\hline
+
+\end{tabular}\caption{./Table/config_exp2 federated attack2}
+\label{tab:./Table/config_exp2 federated attack2}
+\end{table}
diff --git "a/Table/config\\_experiment1\\_attack.tex" "b/Table/config\\_experiment1\\_attack.tex"
new file mode 100644
index 0000000..4e11cc1
--- /dev/null
+++ "b/Table/config\\_experiment1\\_attack.tex"
@@ -0,0 +1,19 @@
+\begin{table}[h]
+\centering
+\begin{tabular}{|c|c|}
+\hline
+parameter & value\\
+\hline
+max_depth & 12 \\\hline
+objective & binary:logistic \\\hline
+tree_method & approx \\\hline
+learning_rate & 0.3 \\\hline
+n_estimators & 20 \\\hline
+gamma & 0.5 \\\hline
+reg_alpha & 0.0 \\\hline
+reg_lambda & 0.1 \\\hline
+model & XGBClassifier \\\hline
+
+\end{tabular}\caption{./Table/config_experiment1_attack}
+\label{tab:./Table/config_experiment1_attack}
+\end{table}
diff --git "a/Table/config\\_experiment1\\_targetandshadow.tex" "b/Table/config\\_experiment1\\_targetandshadow.tex"
new file mode 100644
index 0000000..02a8970
--- /dev/null
+++ "b/Table/config\\_experiment1\\_targetandshadow.tex"
@@ -0,0 +1,17 @@
+\begin{table}[h]
+\centering
+\begin{tabular}{|c|c|}
+\hline
+parameter & value\\
+\hline
+alpha & 0.0 \\\hline
+gamma & 0.5 \\\hline
+lam & 0.1 \\\hline
+learningrate & 0.3 \\\hline
+maxdepth & 8 \\\hline
+maxtree & 20 \\\hline
+model & FederBoost \\\hline
+
+\end{tabular}\caption{./Table/config_experiment1_targetandshadow}
+\label{tab:./Table/config_experiment1_targetandshadow}
+\end{table}
diff --git "a/Table/config\\_experiment2\\_targetandshadow.tex" "b/Table/config\\_experiment2\\_targetandshadow.tex"
new file mode 100644
index 0000000..3d2aec1
--- /dev/null
+++ "b/Table/config\\_experiment2\\_targetandshadow.tex"
@@ -0,0 +1,20 @@
+\begin{table}[h]
+\centering
+\begin{tabular}{|c|c|}
+\hline
+parameter & value\\
+\hline
+alpha & 0.0 \\\hline
+gamma & 0.5 \\\hline
+lam & 0.1 \\\hline
+learningrate & 0.3 \\\hline
+maxdepth & 8 \\\hline
+maxtree & 20 \\\hline
+data_devision & [0.1, 0.9] \\\hline
+nBuckets & 100 \\\hline
+train_size & 10000 \\\hline
+model & FederBoost \\\hline
+
+\end{tabular}\caption{./Table/config_experiment2_targetandshadow}
+\label{tab:./Table/config_experiment2_targetandshadow}
+\end{table}
diff --git a/Table/config_experiment1_targetandshadow.tex b/Table/config_experiment1_targetandshadow.tex
index 60d6456..8f211d9 100644
--- a/Table/config_experiment1_targetandshadow.tex
+++ b/Table/config_experiment1_targetandshadow.tex
@@ -5,10 +5,10 @@
 parameter & value\\
 \hline
 alpha & 0.0 \\\hline
-gamma & 0 \\\hline
+gamma & 0.5 \\\hline
 lam & 0.1 \\\hline
 learningrate & 0.3 \\\hline
-maxdepth & 12 \\\hline
+maxdepth & 8 \\\hline
 maxtree & 20 \\\hline
 model & FederBoost \\\hline
 
diff --git a/Table/experiment_1_alpha.tex b/Table/experiment_1_alpha.tex
index 23c519d..28af037 100644
--- a/Table/experiment_1_alpha.tex
+++ b/Table/experiment_1_alpha.tex
@@ -2,14 +2,14 @@
 \begin{tabular}{|c| *{12}{m{1.0cm}|}}
 \hline\rowcolor{gray!50}
 \cellcolor{gray!80} alpha & \multicolumn{4}{c|}{healthcare} & \multicolumn{4}{c|}{synthetic-10} & \multicolumn{4}{c|}{synthetic-100}\\\hline 
-& accuracy attack & recall attack & accuray target & overfitting target & accuracy attack & recall attack & accuray target & overfitting target & accuracy attack & recall attack & accuray target & overfitting target\\\hline
-0 & 0.62 & 0.66 & 0.37 & 0.54 & 0.71 & 0.79 & 0.66 & 0.34 & 0.84 & 0.88 & 0.12 & 0.88\\ \hline
-0.1 & 0.62 & 0.68 & 0.36 & 0.53 & 0.70 & 0.78 & 0.67 & 0.33 & 0.83 & 0.87 & 0.12 & 0.88\\ \hline
-0.25 & 0.61 & 0.65 & 0.38 & 0.50 & 0.71 & 0.78 & 0.68 & 0.32 & 0.84 & 0.85 & 0.13 & 0.87\\ \hline
-0.5 & 0.61 & 0.64 & 0.38 & 0.49 & 0.69 & 0.75 & 0.68 & 0.32 & 0.82 & 0.86 & 0.12 & 0.88\\ \hline
-0.75 & 0.60 & 0.66 & 0.37 & 0.49 & 0.69 & 0.76 & 0.69 & 0.31 & 0.80 & 0.85 & 0.12 & 0.88\\ \hline
-1 & 0.59 & 0.61 & 0.38 & 0.46 & 0.69 & 0.74 & 0.68 & 0.32 & 0.80 & 0.82 & 0.12 & 0.88\\ \hline
-10 & 0.52 & 0.54 & 0.39 & 0.14 & 0.53 & 0.54 & 0.63 & 0.24 & 0.48 & 0.55 & 0.07 & 0.12\\ \hline
+& accuracy attack & recall attack & accuracy target & overfitting target & accuracy attack & recall attack & accuracy target & overfitting target & accuracy attack & recall attack & accuracy target & overfitting target\\\hline
+0 & 0.51 & 0.45 & 0.22 & 0.19 & 0.55 & 0.52 & 0.70 & 0.21 & 0.70 & 0.71 & 0.21 & 0.77\\ \hline
+0.1 & 0.50 & 0.53 & 0.19 & 0.17 & 0.54 & 0.48 & 0.70 & 0.21 & 0.69 & 0.70 & 0.21 & 0.76\\ \hline
+0.25 & 0.51 & 0.49 & 0.23 & 0.21 & 0.54 & 0.51 & 0.71 & 0.21 & 0.68 & 0.71 & 0.21 & 0.76\\ \hline
+0.5 & 0.51 & 0.48 & 0.22 & 0.18 & 0.54 & 0.50 & 0.72 & 0.21 & 0.66 & 0.67 & 0.21 & 0.73\\ \hline
+0.75 & 0.51 & 0.53 & 0.23 & 0.15 & 0.54 & 0.51 & 0.71 & 0.20 & 0.64 & 0.67 & 0.20 & 0.73\\ \hline
+1 & 0.50 & 0.49 & 0.22 & 0.15 & 0.53 & 0.49 & 0.72 & 0.19 & 0.63 & 0.66 & 0.20 & 0.71\\ \hline
+10 & 0.50 & 0.54 & 0.22 & 0.03 & 0.51 & 0.52 & 0.72 & 0.12 & 0.50 & 0.50 & 0.18 & 0.23\\ \hline
 \end{tabular} 
 \caption{FederBoost-central's attack metrics on alpha.}
 \label{tab:experiment1_alpha}
diff --git a/Table/experiment_1_gamma.tex b/Table/experiment_1_gamma.tex
index a3753f7..5dbedce 100644
--- a/Table/experiment_1_gamma.tex
+++ b/Table/experiment_1_gamma.tex
@@ -2,15 +2,15 @@
 \begin{tabular}{|c| *{12}{m{1.0cm}|}}
 \hline\rowcolor{gray!50}
 \cellcolor{gray!80} gamma & \multicolumn{4}{c|}{healthcare} & \multicolumn{4}{c|}{synthetic-10} & \multicolumn{4}{c|}{synthetic-100}\\\hline 
-& accuracy attack & recall attack & accuray target & overfitting target & accuracy attack & recall attack & accuray target & overfitting target & accuracy attack & recall attack & accuray target & overfitting target\\\hline
-0 & 0.72 & 0.77 & 0.36 & 0.62 & 0.77 & 0.84 & 0.67 & 0.33 & 0.94 & 0.97 & 0.13 & 0.87\\ \hline
-0.1 & 0.71 & 0.76 & 0.39 & 0.59 & 0.76 & 0.83 & 0.66 & 0.34 & 0.94 & 0.96 & 0.13 & 0.87\\ \hline
-0.25 & 0.68 & 0.72 & 0.37 & 0.60 & 0.75 & 0.82 & 0.67 & 0.33 & 0.89 & 0.92 & 0.13 & 0.87\\ \hline
-0.5 & 0.62 & 0.68 & 0.38 & 0.52 & 0.70 & 0.79 & 0.66 & 0.34 & 0.84 & 0.85 & 0.13 & 0.87\\ \hline
-0.75 & 0.59 & 0.65 & 0.36 & 0.50 & 0.68 & 0.75 & 0.68 & 0.32 & 0.80 & 0.84 & 0.12 & 0.87\\ \hline
-1 & 0.56 & 0.61 & 0.37 & 0.43 & 0.67 & 0.73 & 0.66 & 0.33 & 0.76 & 0.78 & 0.12 & 0.87\\ \hline
-5 & 0.50 & 0.38 & 0.39 & 0.13 & 0.55 & 0.56 & 0.63 & 0.21 & 0.49 & 0.59 & 0.09 & 0.36\\ \hline
-10 & 0.50 & 0.49 & 0.38 & 0.05 & 0.52 & 0.55 & 0.58 & 0.15 & 0.50 & 0.76 & 0.07 & 0.13\\ \hline
+& accuracy attack & recall attack & accuracy target & overfitting target & accuracy attack & recall attack & accuracy target & overfitting target & accuracy attack & recall attack & accuracy target & overfitting target\\\hline
+0 & 0.51 & 0.43 & 0.11 & 0.14 & 0.52 & 0.43 & 0.31 & 0.14 & 0.69 & 0.67 & 0.20 & 0.77\\ \hline
+0.1 & 0.51 & 0.35 & 0.11 & 0.13 & 0.52 & 0.44 & 0.39 & 0.17 & 0.70 & 0.70 & 0.20 & 0.76\\ \hline
+0.25 & 0.52 & 0.27 & 0.13 & 0.15 & 0.53 & 0.53 & 0.65 & 0.22 & 0.71 & 0.73 & 0.20 & 0.77\\ \hline
+0.5 & 0.50 & 0.48 & 0.20 & 0.18 & 0.55 & 0.52 & 0.70 & 0.22 & 0.70 & 0.72 & 0.21 & 0.76\\ \hline
+0.75 & 0.51 & 0.48 & 0.26 & 0.23 & 0.55 & 0.56 & 0.74 & 0.19 & 0.69 & 0.72 & 0.21 & 0.76\\ \hline
+1 & 0.51 & 0.47 & 0.31 & 0.23 & 0.55 & 0.57 & 0.76 & 0.18 & 0.66 & 0.71 & 0.20 & 0.76\\ \hline
+5 & 0.50 & 0.61 & 0.37 & 0.07 & 0.51 & 0.51 & 0.77 & 0.11 & 0.52 & 0.54 & 0.18 & 0.38\\ \hline
+10 & 0.51 & 0.51 & 0.39 & 0.05 & 0.52 & 0.50 & 0.75 & 0.08 & 0.50 & 0.49 & 0.15 & 0.20\\ \hline
 \end{tabular} 
 \caption{FederBoost-central's attack metrics on gamma.}
 \label{tab:experiment1_gamma}
diff --git a/Table/experiment_1_lam.tex b/Table/experiment_1_lam.tex
index 56b3d48..8873e69 100644
--- a/Table/experiment_1_lam.tex
+++ b/Table/experiment_1_lam.tex
@@ -2,14 +2,14 @@
 \begin{tabular}{|c| *{12}{m{1.0cm}|}}
 \hline\rowcolor{gray!50}
 \cellcolor{gray!80} lam & \multicolumn{4}{c|}{healthcare} & \multicolumn{4}{c|}{synthetic-10} & \multicolumn{4}{c|}{synthetic-100}\\\hline 
-& accuracy attack & recall attack & accuray target & overfitting target & accuracy attack & recall attack & accuray target & overfitting target & accuracy attack & recall attack & accuray target & overfitting target\\\hline
-0 & 0.63 & 0.70 & 0.36 & 0.55 & 0.71 & 0.74 & 0.66 & 0.34 & 0.84 & 0.88 & 0.12 & 0.88\\ \hline
-0.1 & 0.62 & 0.63 & 0.36 & 0.55 & 0.72 & 0.77 & 0.66 & 0.33 & 0.85 & 0.90 & 0.13 & 0.87\\ \hline
-0.25 & 0.62 & 0.70 & 0.37 & 0.53 & 0.70 & 0.76 & 0.67 & 0.33 & 0.84 & 0.87 & 0.13 & 0.87\\ \hline
-0.5 & 0.60 & 0.66 & 0.37 & 0.52 & 0.71 & 0.78 & 0.67 & 0.33 & 0.84 & 0.89 & 0.12 & 0.88\\ \hline
-0.75 & 0.62 & 0.71 & 0.37 & 0.51 & 0.70 & 0.78 & 0.68 & 0.32 & 0.84 & 0.85 & 0.13 & 0.86\\ \hline
-1 & 0.60 & 0.71 & 0.39 & 0.48 & 0.68 & 0.73 & 0.67 & 0.32 & 0.83 & 0.85 & 0.14 & 0.86\\ \hline
-10 & 0.55 & 0.63 & 0.38 & 0.35 & 0.62 & 0.66 & 0.69 & 0.28 & 0.65 & 0.68 & 0.13 & 0.71\\ \hline
+& accuracy attack & recall attack & accuracy target & overfitting target & accuracy attack & recall attack & accuracy target & overfitting target & accuracy attack & recall attack & accuracy target & overfitting target\\\hline
+0 & 0.52 & 0.46 & 0.22 & 0.19 & 0.54 & 0.52 & 0.70 & 0.21 & 0.70 & 0.74 & 0.20 & 0.78\\ \hline
+0.1 & 0.51 & 0.48 & 0.21 & 0.19 & 0.55 & 0.55 & 0.72 & 0.22 & 0.70 & 0.71 & 0.20 & 0.77\\ \hline
+0.25 & 0.51 & 0.46 & 0.22 & 0.19 & 0.55 & 0.51 & 0.71 & 0.21 & 0.69 & 0.71 & 0.22 & 0.75\\ \hline
+0.5 & 0.51 & 0.44 & 0.24 & 0.18 & 0.54 & 0.50 & 0.73 & 0.20 & 0.68 & 0.67 & 0.22 & 0.72\\ \hline
+0.75 & 0.51 & 0.52 & 0.25 & 0.19 & 0.55 & 0.56 & 0.74 & 0.20 & 0.67 & 0.63 & 0.22 & 0.70\\ \hline
+1 & 0.51 & 0.52 & 0.23 & 0.18 & 0.54 & 0.56 & 0.73 & 0.19 & 0.67 & 0.66 & 0.23 & 0.69\\ \hline
+10 & 0.50 & 0.55 & 0.37 & 0.11 & 0.53 & 0.55 & 0.76 & 0.13 & 0.56 & 0.57 & 0.24 & 0.43\\ \hline
 \end{tabular} 
 \caption{FederBoost-central's attack metrics on lam.}
 \label{tab:experiment1_lam}
diff --git a/Table/experiment_1_learning_rate.tex b/Table/experiment_1_learning_rate.tex
index 489c179..f635877 100644
--- a/Table/experiment_1_learning_rate.tex
+++ b/Table/experiment_1_learning_rate.tex
@@ -2,12 +2,12 @@
 \begin{tabular}{|c| *{12}{m{1.0cm}|}}
 \hline\rowcolor{gray!50}
 \cellcolor{gray!80} learning_rate & \multicolumn{4}{c|}{healthcare} & \multicolumn{4}{c|}{synthetic-10} & \multicolumn{4}{c|}{synthetic-100}\\\hline 
-& accuracy attack & recall attack & accuray target & overfitting target & accuracy attack & recall attack & accuray target & overfitting target & accuracy attack & recall attack & accuray target & overfitting target\\\hline
-0.1 & 0.61 & 0.67 & 0.38 & 0.47 & 0.69 & 0.75 & 0.64 & 0.35 & 0.81 & 0.85 & 0.14 & 0.85\\ \hline
-0.25 & 0.62 & 0.67 & 0.38 & 0.51 & 0.71 & 0.77 & 0.67 & 0.33 & 0.86 & 0.88 & 0.12 & 0.88\\ \hline
-0.5 & 0.58 & 0.61 & 0.37 & 0.52 & 0.69 & 0.74 & 0.65 & 0.35 & 0.79 & 0.83 & 0.12 & 0.88\\ \hline
-0.75 & 0.56 & 0.60 & 0.35 & 0.56 & 0.66 & 0.71 & 0.64 & 0.36 & 0.70 & 0.79 & 0.09 & 0.91\\ \hline
-1 & 0.56 & 0.53 & 0.32 & 0.60 & 0.64 & 0.68 & 0.59 & 0.41 & 0.62 & 0.68 & 0.09 & 0.91\\ \hline
+& accuracy attack & recall attack & accuracy target & overfitting target & accuracy attack & recall attack & accuracy target & overfitting target & accuracy attack & recall attack & accuracy target & overfitting target\\\hline
+0.1 & 0.50 & 0.43 & 0.18 & 0.11 & 0.51 & 0.48 & 0.58 & 0.18 & 0.60 & 0.60 & 0.20 & 0.63\\ \hline
+0.25 & 0.51 & 0.40 & 0.21 & 0.16 & 0.55 & 0.49 & 0.70 & 0.20 & 0.68 & 0.68 & 0.20 & 0.74\\ \hline
+0.5 & 0.52 & 0.50 & 0.20 & 0.25 & 0.57 & 0.55 & 0.75 & 0.22 & 0.68 & 0.74 & 0.19 & 0.81\\ \hline
+0.75 & 0.53 & 0.53 & 0.26 & 0.29 & 0.59 & 0.61 & 0.77 & 0.21 & 0.61 & 0.64 & 0.15 & 0.85\\ \hline
+1 & 0.53 & 0.45 & 0.24 & 0.34 & 0.61 & 0.67 & 0.79 & 0.21 & 0.50 & 1.00 & 0.02 & 0.00\\ \hline
 \end{tabular} 
 \caption{FederBoost-central's attack metrics on learning_rate.}
 \label{tab:experiment1_learning_rate}
diff --git a/Table/experiment_1_max_depth.tex b/Table/experiment_1_max_depth.tex
index ad69d94..70c9b7a 100644
--- a/Table/experiment_1_max_depth.tex
+++ b/Table/experiment_1_max_depth.tex
@@ -2,11 +2,11 @@
 \begin{tabular}{|c| *{12}{m{1.0cm}|}}
 \hline\rowcolor{gray!50}
 \cellcolor{gray!80} max_depth & \multicolumn{4}{c|}{healthcare} & \multicolumn{4}{c|}{synthetic-10} & \multicolumn{4}{c|}{synthetic-100}\\\hline 
-& accuracy attack & recall attack & accuray target & overfitting target & accuracy attack & recall attack & accuray target & overfitting target & accuracy attack & recall attack & accuray target & overfitting target\\\hline
-5 & 0.53 & 0.56 & 0.39 & 0.34 & 0.63 & 0.66 & 0.67 & 0.30 & 0.82 & 0.84 & 0.12 & 0.88\\ \hline
-8 & 0.62 & 0.70 & 0.36 & 0.54 & 0.72 & 0.79 & 0.67 & 0.33 & 0.84 & 0.90 & 0.13 & 0.87\\ \hline
-12 & 0.69 & 0.72 & 0.35 & 0.62 & 0.74 & 0.81 & 0.67 & 0.33 & 0.85 & 0.86 & 0.12 & 0.88\\ \hline
-15 & 0.70 & 0.74 & 0.36 & 0.61 & 0.74 & 0.80 & 0.67 & 0.33 & 0.85 & 0.87 & 0.12 & 0.88\\ \hline
+& accuracy attack & recall attack & accuracy target & overfitting target & accuracy attack & recall attack & accuracy target & overfitting target & accuracy attack & recall attack & accuracy target & overfitting target\\\hline
+5 & 0.50 & 0.43 & 0.11 & 0.01 & 0.50 & 0.34 & 0.10 & -0.00 & 0.50 & 0.54 & 0.12 & 0.25\\ \hline
+8 & 0.51 & 0.41 & 0.20 & 0.19 & 0.55 & 0.52 & 0.71 & 0.21 & 0.70 & 0.72 & 0.21 & 0.77\\ \hline
+12 & 0.67 & 0.66 & 0.34 & 0.60 & 0.65 & 0.71 & 0.80 & 0.20 & 0.78 & 0.86 & 0.22 & 0.78\\ \hline
+15 & 0.73 & 0.75 & 0.36 & 0.62 & 0.66 & 0.72 & 0.81 & 0.19 & 0.78 & 0.85 & 0.22 & 0.78\\ \hline
 \end{tabular} 
 \caption{FederBoost-central's attack metrics on max_depth.}
 \label{tab:experiment1_max_depth}
diff --git a/Table/experiment_1_max_tree.tex b/Table/experiment_1_max_tree.tex
index 0687c09..cbaccef 100644
--- a/Table/experiment_1_max_tree.tex
+++ b/Table/experiment_1_max_tree.tex
@@ -2,14 +2,14 @@
 \begin{tabular}{|c| *{12}{m{1.0cm}|}}
 \hline\rowcolor{gray!50}
 \cellcolor{gray!80} max_tree & \multicolumn{4}{c|}{healthcare} & \multicolumn{4}{c|}{synthetic-10} & \multicolumn{4}{c|}{synthetic-100}\\\hline 
-& accuracy attack & recall attack & accuray target & overfitting target & accuracy attack & recall attack & accuray target & overfitting target & accuracy attack & recall attack & accuray target & overfitting target\\\hline
-5 & 0.56 & 0.60 & 0.35 & 0.43 & 0.64 & 0.67 & 0.61 & 0.36 & 0.66 & 0.72 & 0.10 & 0.82\\ \hline
-10 & 0.59 & 0.66 & 0.36 & 0.52 & 0.68 & 0.74 & 0.66 & 0.34 & 0.82 & 0.86 & 0.12 & 0.88\\ \hline
-20 & 0.62 & 0.71 & 0.37 & 0.54 & 0.72 & 0.78 & 0.67 & 0.33 & 0.84 & 0.84 & 0.12 & 0.88\\ \hline
-30 & 0.64 & 0.62 & 0.37 & 0.55 & 0.71 & 0.79 & 0.67 & 0.33 & 0.84 & 0.90 & 0.12 & 0.88\\ \hline
-50 & 0.63 & 0.59 & 0.37 & 0.53 & 0.71 & 0.77 & 0.66 & 0.34 & 0.84 & 0.86 & 0.13 & 0.87\\ \hline
-100 & 0.62 & 0.60 & 0.37 & 0.52 & 0.70 & 0.78 & 0.67 & 0.32 & 0.84 & 0.87 & 0.12 & 0.88\\ \hline
-150 & 0.63 & 0.61 & 0.36 & 0.55 & 0.70 & 0.75 & 0.66 & 0.34 & 0.85 & 0.86 & 0.12 & 0.88\\ \hline
+& accuracy attack & recall attack & accuracy target & overfitting target & accuracy attack & recall attack & accuracy target & overfitting target & accuracy attack & recall attack & accuracy target & overfitting target\\\hline
+5 & 0.50 & 0.50 & 0.17 & 0.09 & 0.50 & 0.48 & 0.52 & 0.14 & 0.53 & 0.56 & 0.17 & 0.45\\ \hline
+10 & 0.50 & 0.49 & 0.17 & 0.14 & 0.52 & 0.47 & 0.61 & 0.20 & 0.59 & 0.60 & 0.18 & 0.66\\ \hline
+20 & 0.51 & 0.49 & 0.22 & 0.19 & 0.55 & 0.49 & 0.69 & 0.21 & 0.70 & 0.71 & 0.21 & 0.77\\ \hline
+30 & 0.52 & 0.44 & 0.20 & 0.23 & 0.57 & 0.54 & 0.73 & 0.22 & 0.75 & 0.80 & 0.22 & 0.77\\ \hline
+50 & 0.53 & 0.49 & 0.23 & 0.26 & 0.60 & 0.59 & 0.78 & 0.20 & 0.76 & 0.84 & 0.22 & 0.78\\ \hline
+100 & 0.54 & 0.39 & 0.25 & 0.31 & 0.62 & 0.67 & 0.81 & 0.18 & 0.77 & 0.83 & 0.22 & 0.78\\ \hline
+150 & 0.55 & 0.38 & 0.27 & 0.35 & 0.63 & 0.66 & 0.82 & 0.18 & 0.76 & 0.82 & 0.22 & 0.78\\ \hline
 \end{tabular} 
 \caption{FederBoost-central's attack metrics on max_tree.}
 \label{tab:experiment1_max_tree}
diff --git a/Table/experiment_1_nBuckets.tex b/Table/experiment_1_nBuckets.tex
new file mode 100644
index 0000000..fde2843
--- /dev/null
+++ b/Table/experiment_1_nBuckets.tex
@@ -0,0 +1,14 @@
+\begin{table*}[]\centering
+\begin{tabular}{|c| *{12}{m{1.0cm}|}}
+\hline\rowcolor{gray!50}
+\cellcolor{gray!80} nBuckets & \multicolumn{4}{c|}{healthcare} & \multicolumn{4}{c|}{synthetic-10} & \multicolumn{4}{c|}{synthetic-100}\\\hline 
+& accuracy attack & recall attack & accuracy target & overfitting target & accuracy attack & recall attack & accuracy target & overfitting target & accuracy attack & recall attack & accuracy target & overfitting target\\\hline
+10 & 0.50 & 0.51 & 0.21 & 0.17 & 0.53 & 0.53 & 0.72 & 0.17 & 0.62 & 0.62 & 0.20 & 0.72\\ \hline
+20 & 0.50 & 0.48 & 0.21 & 0.18 & 0.53 & 0.55 & 0.73 & 0.19 & 0.66 & 0.67 & 0.21 & 0.75\\ \hline
+50 & 0.51 & 0.44 & 0.22 & 0.19 & 0.54 & 0.53 & 0.73 & 0.20 & 0.69 & 0.71 & 0.21 & 0.76\\ \hline
+100 & 0.51 & 0.42 & 0.23 & 0.19 & 0.55 & 0.52 & 0.70 & 0.21 & 0.70 & 0.74 & 0.21 & 0.77\\ \hline
+1000 & 0.51 & 0.44 & 0.21 & 0.19 & 0.55 & 0.54 & 0.69 & 0.23 & 0.71 & 0.73 & 0.20 & 0.78\\ \hline
+\end{tabular} 
+\caption{FederBoost-central's attack metrics on nBuckets.}
+\label{tab:experiment1_nBuckets}
+\end{table*}
diff --git a/Table/experiment_1_train_size.tex b/Table/experiment_1_train_size.tex
new file mode 100644
index 0000000..28cc5c2
--- /dev/null
+++ b/Table/experiment_1_train_size.tex
@@ -0,0 +1,13 @@
+\begin{table*}[]\centering
+\begin{tabular}{|c| *{12}{m{1.0cm}|}}
+\hline\rowcolor{gray!50}
+\cellcolor{gray!80} train_size & \multicolumn{4}{c|}{healthcare} & \multicolumn{4}{c|}{synthetic-10} & \multicolumn{4}{c|}{synthetic-100}\\\hline 
+& accuracy attack & recall attack & accuracy target & overfitting target & accuracy attack & recall attack & accuracy target & overfitting target & accuracy attack & recall attack & accuracy target & overfitting target\\\hline
+1000 & 0.67 & 0.71 & 0.31 & 0.64 & 0.72 & 0.76 & 0.58 & 0.42 & 0.85 & 0.90 & 0.07 & 0.93\\ \hline
+2000 & 0.59 & 0.66 & 0.31 & 0.60 & 0.69 & 0.70 & 0.61 & 0.39 & 0.84 & 0.89 & 0.12 & 0.88\\ \hline
+5000 & 0.54 & 0.54 & 0.28 & 0.34 & 0.60 & 0.57 & 0.69 & 0.28 & 0.77 & 0.80 & 0.19 & 0.80\\ \hline
+10000 & 0.51 & 0.43 & 0.23 & 0.19 & 0.54 & 0.52 & 0.72 & 0.20 & 0.70 & 0.72 & 0.20 & 0.77\\ \hline
+\end{tabular} 
+\caption{FederBoost-central's attack metrics on train_size.}
+\label{tab:experiment1_train_size}
+\end{table*}
diff --git a/environment.txt b/environment.txt
new file mode 100644
index 0000000..7ab8731
--- /dev/null
+++ b/environment.txt
@@ -0,0 +1,173 @@
+# This file may be used to create an environment using:
+# $ conda create --name <env> --file <this file>
+# platform: linux-64
+@EXPLICIT
+https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2
+https://repo.anaconda.com/pkgs/main/linux-64/_py-xgboost-mutex-2.0-cpu_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/blas-1.0-mkl.conda
+https://repo.anaconda.com/pkgs/main/linux-64/ca-certificates-2023.08.22-h06a4308_0.conda
+https://repo.anaconda.com/pkgs/main/noarch/font-ttf-dejavu-sans-mono-2.37-hd3eb1b0_0.conda
+https://repo.anaconda.com/pkgs/main/noarch/font-ttf-inconsolata-2.001-hcb22688_0.conda
+https://repo.anaconda.com/pkgs/main/noarch/font-ttf-source-code-pro-2.030-hd3eb1b0_0.conda
+https://repo.anaconda.com/pkgs/main/noarch/font-ttf-ubuntu-0.83-h8b1ccd4_0.conda
+https://conda.anaconda.org/conda-forge/noarch/kernel-headers_linux-64-2.6.32-he073ed8_15.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.40-h41732ed_0.conda
+https://conda.anaconda.org/conda-forge/linux-64/libgcc-devel_linux-64-13.1.0-he3cc6c4_0.conda
+https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-13.1.0-h15d22d2_0.conda
+https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-devel_linux-64-13.1.0-he3cc6c4_0.conda
+https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-13.1.0-hfd8a6a1_0.conda
+https://conda.anaconda.org/conda-forge/linux-64/mpi-1.0-openmpi.tar.bz2
+https://repo.anaconda.com/pkgs/main/linux-64/poppler-data-0.4.11-h06a4308_1.conda
+https://conda.anaconda.org/conda-forge/noarch/rust-std-x86_64-unknown-linux-gnu-1.73.0-hc1431ca_0.conda
+https://repo.anaconda.com/pkgs/main/noarch/fonts-anaconda-1-h8fa9717_0.conda
+https://conda.anaconda.org/conda-forge/linux-64/libgfortran-ng-13.1.0-h69a702a_0.conda
+https://conda.anaconda.org/conda-forge/linux-64/libgomp-13.1.0-he5830b7_0.conda
+https://conda.anaconda.org/conda-forge/noarch/sysroot_linux-64-2.12-he073ed8_15.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.40-hf600244_0.conda
+https://repo.anaconda.com/pkgs/main/noarch/fonts-conda-ecosystem-1-hd3eb1b0_0.conda
+https://conda.anaconda.org/conda-forge/linux-64/binutils_linux-64-2.40-hbdbef99_0.conda
+https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-13.1.0-he5830b7_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/libxgboost-1.7.3-h6a678d5_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/bzip2-1.0.8-h7b6447c_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/c-ares-1.19.1-h5eee18b_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/expat-2.4.9-h6a678d5_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/fribidi-1.0.10-h7b6447c_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/giflib-5.2.1-h5eee18b_3.conda
+https://repo.anaconda.com/pkgs/main/linux-64/graphite2-1.3.14-h295c915_1.conda
+https://repo.anaconda.com/pkgs/main/linux-64/icu-58.2-he6710b0_3.conda
+https://repo.anaconda.com/pkgs/main/linux-64/jpeg-9e-h5eee18b_1.conda
+https://repo.anaconda.com/pkgs/main/linux-64/lerc-3.0-h295c915_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/libbrotlicommon-1.0.9-h5eee18b_7.conda
+https://repo.anaconda.com/pkgs/main/linux-64/libdeflate-1.17-h5eee18b_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/libev-4.33-h7f8727e_1.conda
+https://repo.anaconda.com/pkgs/main/linux-64/libffi-3.4.4-h6a678d5_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/libiconv-1.16-h7f8727e_2.conda
+https://conda.anaconda.org/conda-forge/linux-64/libsanitizer-13.1.0-hfd8a6a1_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/libtool-2.4.6-h6a678d5_1009.conda
+https://repo.anaconda.com/pkgs/main/linux-64/libuuid-1.41.5-h5eee18b_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/libwebp-base-1.2.4-h5eee18b_1.conda
+https://repo.anaconda.com/pkgs/main/linux-64/libxcb-1.15-h7f8727e_0.conda
+https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.2.13-hd590300_5.conda
+https://repo.anaconda.com/pkgs/main/linux-64/lz4-c-1.9.4-h6a678d5_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/ncurses-6.4-h6a678d5_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/ninja-base-1.10.2-hd09550d_5.conda
+https://repo.anaconda.com/pkgs/main/linux-64/nspr-4.35-h6a678d5_0.conda
+https://conda.anaconda.org/conda-forge/linux-64/openssl-3.1.3-hd590300_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/pcre-8.45-h295c915_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/pixman-0.40.0-h7f8727e_1.conda
+https://repo.anaconda.com/pkgs/main/linux-64/tbb-2021.8.0-hdb19cb5_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/xz-5.4.2-h5eee18b_0.conda
+https://conda.anaconda.org/conda-forge/linux-64/gcc_impl_linux-64-13.1.0-hc4be1a9_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/libbrotlidec-1.0.9-h5eee18b_7.conda
+https://repo.anaconda.com/pkgs/main/linux-64/libbrotlienc-1.0.9-h5eee18b_7.conda
+https://repo.anaconda.com/pkgs/main/linux-64/libedit-3.1.20221030-h5eee18b_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/libevent-2.1.12-hdbd6064_1.conda
+https://repo.anaconda.com/pkgs/main/linux-64/libssh2-1.10.0-hdbd6064_2.conda
+https://conda.anaconda.org/conda-forge/linux-64/nano-7.2-h8228510_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/readline-8.2-h5eee18b_0.conda
+https://conda.anaconda.org/conda-forge/linux-64/zlib-1.2.13-hd590300_5.conda
+https://repo.anaconda.com/pkgs/main/linux-64/brotli-bin-1.0.9-h5eee18b_7.conda
+https://conda.anaconda.org/conda-forge/linux-64/gcc-13.1.0-h8e92de4_0.conda
+https://conda.anaconda.org/conda-forge/linux-64/gcc_linux-64-13.1.0-hd2d1137_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/glib-2.69.1-he621ea3_2.conda
+https://conda.anaconda.org/conda-forge/linux-64/gxx_impl_linux-64-13.1.0-hc4be1a9_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/intel-openmp-2023.1.0-hdb19cb5_46305.conda
+https://repo.anaconda.com/pkgs/main/linux-64/krb5-1.20.1-h143b758_1.conda
+https://repo.anaconda.com/pkgs/main/linux-64/libllvm10-10.0.1-hbcb73fb_5.conda
+https://repo.anaconda.com/pkgs/main/linux-64/libnghttp2-1.52.0-h2d74bed_1.conda
+https://repo.anaconda.com/pkgs/main/linux-64/libpng-1.6.39-h5eee18b_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/libxml2-2.9.14-h74e7548_0.conda
+https://conda.anaconda.org/conda-forge/linux-64/openmpi-4.1.5-h414af15_101.conda
+https://conda.anaconda.org/conda-forge/linux-64/rust-1.73.0-h70c747d_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/sqlite-3.41.2-h5eee18b_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/tk-8.6.12-h1ccaba5_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/zstd-1.5.5-hc292b87_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/brotli-1.0.9-h5eee18b_7.conda
+https://repo.anaconda.com/pkgs/main/linux-64/dbus-1.13.18-hb2f20db_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/freetype-2.12.1-h4a9f257_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/gstreamer-1.14.1-h5eee18b_1.conda
+https://repo.anaconda.com/pkgs/main/linux-64/gts-0.7.6-hb67d8dd_3.conda
+https://conda.anaconda.org/conda-forge/linux-64/gxx_linux-64-13.1.0-h5006d2a_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/libboost-1.73.0-h28710b8_12.conda
+https://repo.anaconda.com/pkgs/main/linux-64/libclang-10.0.1-default_hb85057a_2.conda
+https://repo.anaconda.com/pkgs/main/linux-64/libcurl-8.2.1-h251f7ec_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/libpq-12.15-hdbd6064_1.conda
+https://repo.anaconda.com/pkgs/main/linux-64/libtiff-4.5.0-h6a678d5_2.conda
+https://repo.anaconda.com/pkgs/main/linux-64/libxkbcommon-1.0.1-hfa300c1_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/libxslt-1.1.35-h4e12654_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/mkl-2023.1.0-h6d00ec8_46342.conda
+https://repo.anaconda.com/pkgs/main/linux-64/nss-3.89.1-h6a678d5_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/python-3.8.16-h955ad1f_4.conda
+https://repo.anaconda.com/pkgs/main/noarch/appdirs-1.4.4-pyhd3eb1b0_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/boost-cpp-1.73.0-h7f8727e_12.conda
+https://conda.anaconda.org/conda-forge/noarch/certifi-2023.7.22-pyhd8ed1ab_0.conda
+https://repo.anaconda.com/pkgs/main/noarch/charset-normalizer-2.0.4-pyhd3eb1b0_0.conda
+https://repo.anaconda.com/pkgs/main/noarch/cycler-0.11.0-pyhd3eb1b0_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/fontconfig-2.14.1-h52c9d5c_1.conda
+https://repo.anaconda.com/pkgs/main/linux-64/gdk-pixbuf-2.42.10-h5eee18b_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/gobject-introspection-1.72.0-py38hbb6d50b_2.conda
+https://repo.anaconda.com/pkgs/main/linux-64/gst-plugins-base-1.14.1-h6a678d5_1.conda
+https://repo.anaconda.com/pkgs/main/linux-64/idna-3.4-py38h06a4308_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/joblib-1.2.0-py38h06a4308_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/kiwisolver-1.4.2-py38h295c915_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/lcms2-2.12-h3be6417_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/libwebp-1.2.4-h11a3e52_1.conda
+https://repo.anaconda.com/pkgs/main/linux-64/mkl-service-2.4.0-py38h5eee18b_1.conda
+https://repo.anaconda.com/pkgs/main/noarch/munkres-1.1.4-py_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/ninja-1.10.2-h06a4308_5.conda
+https://repo.anaconda.com/pkgs/main/linux-64/openjpeg-2.4.0-h3ad879b_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/ply-3.11-py38_0.conda
+https://repo.anaconda.com/pkgs/main/noarch/pycparser-2.21-pyhd3eb1b0_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/pyparsing-3.0.9-py38h06a4308_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/pysocks-1.7.1-py38h06a4308_0.conda
+https://conda.anaconda.org/conda-forge/linux-64/python_abi-3.8-2_cp38.tar.bz2
+https://repo.anaconda.com/pkgs/main/linux-64/pytz-2022.1-py38h06a4308_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/setuptools-67.8.0-py38h06a4308_0.conda
+https://repo.anaconda.com/pkgs/main/noarch/six-1.16.0-pyhd3eb1b0_1.conda
+https://repo.anaconda.com/pkgs/main/noarch/threadpoolctl-2.2.0-pyh0d69192_0.conda
+https://repo.anaconda.com/pkgs/main/noarch/toml-0.10.2-pyhd3eb1b0_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/tornado-6.2-py38h5eee18b_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/wheel-0.38.4-py38h06a4308_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/atk-1.0-2.36.0-ha1a6a79_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/cairo-1.16.0-hb05425b_5.conda
+https://repo.anaconda.com/pkgs/main/linux-64/cffi-1.15.1-py38h5eee18b_3.conda
+https://repo.anaconda.com/pkgs/main/noarch/fonttools-4.25.0-pyhd3eb1b0_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/libgd-2.3.3-h6a678d5_3.conda
+https://conda.anaconda.org/conda-forge/linux-64/mpi4py-3.1.4-py38h64caa1c_0.tar.bz2
+https://repo.anaconda.com/pkgs/main/linux-64/numpy-base-1.24.3-py38h060ed82_1.conda
+https://repo.anaconda.com/pkgs/main/noarch/packaging-21.3-pyhd3eb1b0_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/pillow-9.4.0-py38h6a678d5_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/pip-23.1.2-py38h06a4308_0.conda
+https://repo.anaconda.com/pkgs/main/noarch/python-dateutil-2.8.2-pyhd3eb1b0_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/qt-main-5.15.2-h327a75a_7.conda
+https://repo.anaconda.com/pkgs/main/linux-64/brotlipy-0.7.0-py38h27cfd23_1003.conda
+https://repo.anaconda.com/pkgs/main/linux-64/cryptography-39.0.1-py38h9ce1e76_2.conda
+https://repo.anaconda.com/pkgs/main/linux-64/harfbuzz-4.3.0-hf52aaf7_1.conda
+https://repo.anaconda.com/pkgs/main/linux-64/poppler-22.12.0-h9614445_1.conda
+https://repo.anaconda.com/pkgs/main/linux-64/qt-webengine-5.15.9-hd2b0992_4.conda
+https://repo.anaconda.com/pkgs/main/linux-64/sip-6.6.2-py38h6a678d5_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/pango-1.50.7-h05da053_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/pyopenssl-23.0.0-py38h06a4308_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/pyqt5-sip-12.11.0-py38h6a678d5_1.conda
+https://repo.anaconda.com/pkgs/main/linux-64/qtwebkit-5.212-h4eab89a_4.conda
+https://repo.anaconda.com/pkgs/main/linux-64/gtk2-2.24.33-h73c1081_2.conda
+https://repo.anaconda.com/pkgs/main/linux-64/librsvg-2.54.4-h36cc946_2.conda
+https://repo.anaconda.com/pkgs/main/linux-64/pyqt-5.15.7-py38h6a678d5_1.conda
+https://repo.anaconda.com/pkgs/main/linux-64/urllib3-1.26.16-py38h06a4308_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/graphviz-2.50.0-h1b29801_1.conda
+https://repo.anaconda.com/pkgs/main/linux-64/requests-2.29.0-py38h06a4308_0.conda
+https://repo.anaconda.com/pkgs/main/noarch/pooch-1.4.0-pyhd3eb1b0_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/bottleneck-1.3.5-py38h7deecbd_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/matplotlib-3.5.1-py38h06a4308_1.conda
+https://repo.anaconda.com/pkgs/main/linux-64/matplotlib-base-3.5.1-py38ha18d171_1.conda
+https://repo.anaconda.com/pkgs/main/linux-64/mkl_fft-1.3.6-py38h417a72b_1.conda
+https://repo.anaconda.com/pkgs/main/linux-64/mkl_random-1.2.2-py38h417a72b_1.conda
+https://repo.anaconda.com/pkgs/main/linux-64/numpy-1.24.3-py38hf6e8229_1.conda
+https://repo.anaconda.com/pkgs/main/linux-64/numexpr-2.8.4-py38hc78ab66_1.conda
+https://repo.anaconda.com/pkgs/main/linux-64/scipy-1.10.1-py38hf6e8229_1.conda
+https://repo.anaconda.com/pkgs/main/linux-64/pandas-1.4.2-py38h295c915_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/scikit-learn-1.2.2-py38h6a678d5_1.conda
+https://conda.anaconda.org/conda-forge/noarch/imbalanced-learn-0.11.0-pyhd8ed1ab_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/py-xgboost-1.7.3-py38h06a4308_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/xgboost-1.7.3-py38h06a4308_0.conda
diff --git a/environment.yml b/environment.yml
new file mode 100644
index 0000000..0a9fab1
--- /dev/null
+++ b/environment.yml
@@ -0,0 +1,259 @@
+name: fedxgboost_mpi
+channels:
+  - conda-forge
+  - defaults
+dependencies:
+  - _libgcc_mutex=0.1=conda_forge
+  - _openmp_mutex=4.5=2_gnu
+  - _py-xgboost-mutex=2.0=cpu_0
+  - appdirs=1.4.4=pyhd3eb1b0_0
+  - atk-1.0=2.36.0=ha1a6a79_0
+  - binutils_impl_linux-64=2.40=hf600244_0
+  - binutils_linux-64=2.40=hbdbef99_0
+  - blas=1.0=mkl
+  - boost-cpp=1.73.0=h7f8727e_12
+  - bottleneck=1.3.5=py38h7deecbd_0
+  - brotli=1.0.9=h5eee18b_7
+  - brotli-bin=1.0.9=h5eee18b_7
+  - brotlipy=0.7.0=py38h27cfd23_1003
+  - bzip2=1.0.8=h7b6447c_0
+  - c-ares=1.19.1=h5eee18b_0
+  - ca-certificates=2023.08.22=h06a4308_0
+  - cairo=1.16.0=hb05425b_5
+  - certifi=2023.7.22=pyhd8ed1ab_0
+  - cffi=1.15.1=py38h5eee18b_3
+  - charset-normalizer=2.0.4=pyhd3eb1b0_0
+  - cryptography=39.0.1=py38h9ce1e76_2
+  - cycler=0.11.0=pyhd3eb1b0_0
+  - dbus=1.13.18=hb2f20db_0
+  - expat=2.4.9=h6a678d5_0
+  - font-ttf-dejavu-sans-mono=2.37=hd3eb1b0_0
+  - font-ttf-inconsolata=2.001=hcb22688_0
+  - font-ttf-source-code-pro=2.030=hd3eb1b0_0
+  - font-ttf-ubuntu=0.83=h8b1ccd4_0
+  - fontconfig=2.14.1=h52c9d5c_1
+  - fonts-anaconda=1=h8fa9717_0
+  - fonts-conda-ecosystem=1=hd3eb1b0_0
+  - fonttools=4.25.0=pyhd3eb1b0_0
+  - freetype=2.12.1=h4a9f257_0
+  - fribidi=1.0.10=h7b6447c_0
+  - gcc=13.1.0=h8e92de4_0
+  - gcc_impl_linux-64=13.1.0=hc4be1a9_0
+  - gcc_linux-64=13.1.0=hd2d1137_0
+  - gdk-pixbuf=2.42.10=h5eee18b_0
+  - giflib=5.2.1=h5eee18b_3
+  - glib=2.69.1=he621ea3_2
+  - gobject-introspection=1.72.0=py38hbb6d50b_2
+  - graphite2=1.3.14=h295c915_1
+  - graphviz=2.50.0=h1b29801_1
+  - gst-plugins-base=1.14.1=h6a678d5_1
+  - gstreamer=1.14.1=h5eee18b_1
+  - gtk2=2.24.33=h73c1081_2
+  - gts=0.7.6=hb67d8dd_3
+  - gxx_impl_linux-64=13.1.0=hc4be1a9_0
+  - gxx_linux-64=13.1.0=h5006d2a_0
+  - harfbuzz=4.3.0=hf52aaf7_1
+  - icu=58.2=he6710b0_3
+  - idna=3.4=py38h06a4308_0
+  - imbalanced-learn=0.11.0=pyhd8ed1ab_0
+  - intel-openmp=2023.1.0=hdb19cb5_46305
+  - joblib=1.2.0=py38h06a4308_0
+  - jpeg=9e=h5eee18b_1
+  - kernel-headers_linux-64=2.6.32=he073ed8_15
+  - kiwisolver=1.4.2=py38h295c915_0
+  - krb5=1.20.1=h143b758_1
+  - lcms2=2.12=h3be6417_0
+  - ld_impl_linux-64=2.40=h41732ed_0
+  - lerc=3.0=h295c915_0
+  - libboost=1.73.0=h28710b8_12
+  - libbrotlicommon=1.0.9=h5eee18b_7
+  - libbrotlidec=1.0.9=h5eee18b_7
+  - libbrotlienc=1.0.9=h5eee18b_7
+  - libcurl=8.2.1=h251f7ec_0
+  - libdeflate=1.17=h5eee18b_0
+  - libedit=3.1.20221030=h5eee18b_0
+  - libev=4.33=h7f8727e_1
+  - libevent=2.1.12=hdbd6064_1
+  - libffi=3.4.4=h6a678d5_0
+  - libgcc-devel_linux-64=13.1.0=he3cc6c4_0
+  - libgcc-ng=13.1.0=he5830b7_0
+  - libgd=2.3.3=h6a678d5_3
+  - libgfortran-ng=13.1.0=h69a702a_0
+  - libgfortran5=13.1.0=h15d22d2_0
+  - libgomp=13.1.0=he5830b7_0
+  - libiconv=1.16=h7f8727e_2
+  - libllvm10=10.0.1=hbcb73fb_5
+  - libnghttp2=1.52.0=h2d74bed_1
+  - libpng=1.6.39=h5eee18b_0
+  - libpq=12.15=hdbd6064_1
+  - librsvg=2.54.4=h36cc946_2
+  - libsanitizer=13.1.0=hfd8a6a1_0
+  - libssh2=1.10.0=hdbd6064_2
+  - libstdcxx-devel_linux-64=13.1.0=he3cc6c4_0
+  - libstdcxx-ng=13.1.0=hfd8a6a1_0
+  - libtiff=4.5.0=h6a678d5_2
+  - libtool=2.4.6=h6a678d5_1009
+  - libuuid=1.41.5=h5eee18b_0
+  - libwebp=1.2.4=h11a3e52_1
+  - libwebp-base=1.2.4=h5eee18b_1
+  - libxcb=1.15=h7f8727e_0
+  - libxgboost=1.7.3=h6a678d5_0
+  - libxkbcommon=1.0.1=hfa300c1_0
+  - libxml2=2.9.14=h74e7548_0
+  - libxslt=1.1.35=h4e12654_0
+  - libzlib=1.2.13=hd590300_5
+  - lz4-c=1.9.4=h6a678d5_0
+  - matplotlib=3.5.1=py38h06a4308_1
+  - matplotlib-base=3.5.1=py38ha18d171_1
+  - mkl=2023.1.0=h6d00ec8_46342
+  - mkl-service=2.4.0=py38h5eee18b_1
+  - mkl_fft=1.3.6=py38h417a72b_1
+  - mkl_random=1.2.2=py38h417a72b_1
+  - mpi=1.0=openmpi
+  - mpi4py=3.1.4=py38h64caa1c_0
+  - munkres=1.1.4=py_0
+  - nano=7.2=h8228510_0
+  - ncurses=6.4=h6a678d5_0
+  - ninja=1.10.2=h06a4308_5
+  - ninja-base=1.10.2=hd09550d_5
+  - nspr=4.35=h6a678d5_0
+  - nss=3.89.1=h6a678d5_0
+  - numexpr=2.8.4=py38hc78ab66_1
+  - numpy=1.24.3=py38hf6e8229_1
+  - numpy-base=1.24.3=py38h060ed82_1
+  - openjpeg=2.4.0=h3ad879b_0
+  - openmpi=4.1.5=h414af15_101
+  - openssl=3.1.3=hd590300_0
+  - packaging=21.3=pyhd3eb1b0_0
+  - pandas=1.4.2=py38h295c915_0
+  - pango=1.50.7=h05da053_0
+  - pcre=8.45=h295c915_0
+  - pillow=9.4.0=py38h6a678d5_0
+  - pip=23.1.2=py38h06a4308_0
+  - pixman=0.40.0=h7f8727e_1
+  - ply=3.11=py38_0
+  - pooch=1.4.0=pyhd3eb1b0_0
+  - poppler=22.12.0=h9614445_1
+  - poppler-data=0.4.11=h06a4308_1
+  - py-xgboost=1.7.3=py38h06a4308_0
+  - pycparser=2.21=pyhd3eb1b0_0
+  - pyopenssl=23.0.0=py38h06a4308_0
+  - pyparsing=3.0.9=py38h06a4308_0
+  - pyqt=5.15.7=py38h6a678d5_1
+  - pyqt5-sip=12.11.0=py38h6a678d5_1
+  - pysocks=1.7.1=py38h06a4308_0
+  - python=3.8.16=h955ad1f_4
+  - python-dateutil=2.8.2=pyhd3eb1b0_0
+  - python_abi=3.8=2_cp38
+  - pytz=2022.1=py38h06a4308_0
+  - qt-main=5.15.2=h327a75a_7
+  - qt-webengine=5.15.9=hd2b0992_4
+  - qtwebkit=5.212=h4eab89a_4
+  - readline=8.2=h5eee18b_0
+  - requests=2.29.0=py38h06a4308_0
+  - rust=1.73.0=h70c747d_0
+  - rust-std-x86_64-unknown-linux-gnu=1.73.0=hc1431ca_0
+  - scikit-learn=1.2.2=py38h6a678d5_1
+  - scipy=1.10.1=py38hf6e8229_1
+  - setuptools=67.8.0=py38h06a4308_0
+  - sip=6.6.2=py38h6a678d5_0
+  - six=1.16.0=pyhd3eb1b0_1
+  - sqlite=3.41.2=h5eee18b_0
+  - sysroot_linux-64=2.12=he073ed8_15
+  - tbb=2021.8.0=hdb19cb5_0
+  - threadpoolctl=2.2.0=pyh0d69192_0
+  - tk=8.6.12=h1ccaba5_0
+  - toml=0.10.2=pyhd3eb1b0_0
+  - tornado=6.2=py38h5eee18b_0
+  - urllib3=1.26.16=py38h06a4308_0
+  - wheel=0.38.4=py38h06a4308_0
+  - xgboost=1.7.3=py38h06a4308_0
+  - xz=5.4.2=h5eee18b_0
+  - zlib=1.2.13=hd590300_5
+  - zstd=1.5.5=hc292b87_0
+  - pip:
+      - absl-py==1.4.0
+      - astunparse==1.6.3
+      - baidu-aip==4.16.12
+      - cachetools==5.3.1
+      - click==8.1.7
+      - cmake==3.27.2
+      - cssselect==1.2.0
+      - ddsketch==2.0.4
+      - dlib==19.24.2
+      - dnspython==2.4.2
+      - face-recognition==1.3.0
+      - face-recognition-models==0.3.0
+      - filelock==3.12.2
+      - flatbuffers==23.5.26
+      - gast==0.4.0
+      - google-auth==2.23.0
+      - google-auth-oauthlib==1.0.0
+      - google-pasta==0.2.0
+      - grpcio==1.58.0
+      - h5py==3.9.0
+      - importlib-metadata==6.8.0
+      - jieba==0.42.1
+      - jinja2==3.1.2
+      - keras==2.13.1
+      - libclang==16.0.6
+      - lit==16.0.6
+      - lxml==4.9.3
+      - markdown==3.4.4
+      - markupsafe==2.1.3
+      - maturin==1.3.0
+      - memory-profiler==0.61.0
+      - mpmath==1.3.0
+      - networkx==3.1
+      - nvidia-cublas-cu11==11.10.3.66
+      - nvidia-cuda-cupti-cu11==11.7.101
+      - nvidia-cuda-nvrtc-cu11==11.7.99
+      - nvidia-cuda-runtime-cu11==11.7.99
+      - nvidia-cudnn-cu11==8.5.0.96
+      - nvidia-cufft-cu11==10.9.0.58
+      - nvidia-curand-cu11==10.2.10.91
+      - nvidia-cusolver-cu11==11.4.0.1
+      - nvidia-cusparse-cu11==11.7.4.91
+      - nvidia-nccl-cu11==2.14.3
+      - nvidia-nvtx-cu11==11.7.91
+      - oauthlib==3.2.2
+      - opencv-python==4.8.1.78
+      - opt-einsum==3.3.0
+      - phe==1.5.0
+      - protobuf==4.24.2
+      - psutil==5.9.5
+      - pyasn1==0.5.0
+      - pyasn1-modules==0.3.0
+      - pydp==0.1.16
+      - pydub==0.25.1
+      - pymongo==4.5.0
+      - pymysql==1.1.0
+      - pyquery==2.0.0
+      - python-chess==0.27.0
+      - python-dp==1.1.1
+      - python-graphviz==0.20.1
+      - pyyaml==6.0.1
+      - requests-oauthlib==1.3.1
+      - rsa==4.9
+      - sds==2.0.1
+      - sds-pyo3==1.9.0
+      - seaborn==0.13.0
+      - sfxgboost==0.1.0
+      - sympy==1.12
+      - tensorboard==2.13.0
+      - tensorboard-data-server==0.7.1
+      - tensorflow==2.13.0
+      - tensorflow-estimator==2.13.0
+      - tensorflow-io-gcs-filesystem==0.34.0
+      - termcolor==2.3.0
+      - tomli==2.0.1
+      - torch==2.0.1
+      - tqdm==4.66.1
+      - triton==2.0.0
+      - typing-extensions==4.5.0
+      - ucimlrepo==0.0.2
+      - werkzeug==2.3.7
+      - wrapt==1.15.0
+      - xpinyin==0.7.6
+      - zipp==3.16.2
+prefix: /home/meerhofj/.conda/envs/fedxgboost_mpi
diff --git a/environment_hist.yml b/environment_hist.yml
new file mode 100644
index 0000000..4fe52be
--- /dev/null
+++ b/environment_hist.yml
@@ -0,0 +1,31 @@
+name: fedxgboost_mpi
+channels:
+  - defaults
+dependencies:
+  - python=3.8
+  - packaging=21.3
+  - fonttools
+  - numpy
+  - kiwisolver=1.4.2
+  - pandas=1.4.2
+  - matplotlib=3.5.1
+  - pytz=2022.1
+  - mpi4py
+  - six=1.16.0
+  - pillow
+  - python-dateutil=2.8.2
+  - scipy
+  - xgboost
+  - cycler=0.11.0
+  - pyparsing
+  - ca-certificates
+  - certifi
+  - openssl
+  - openmpi
+  - gxx_linux-64
+  - gcc
+  - nano
+  - graphviz
+  - imbalanced-learn
+  - rust
+prefix: /home/meerhofj/.conda/envs/fedxgboost_mpi
diff --git a/run_experiment.sh b/run_experiment.sh
index fe9ba15..7ec424f 100755
--- a/run_experiment.sh
+++ b/run_experiment.sh
@@ -6,10 +6,10 @@ errorfile="error_${name}${experiment}.txt"
 
 case "$experiment" in
     "1")
-        rm $outputfile $errorfile & pip install . && bsub -J $name -q bio -n 10 -M 32G -R "rusage[mem=32G]" -o $outputfile -e $errorfile mpiexec -np 5 /home/meerhofj/.conda/envs/fedxgboost_mpi/bin/python tests/main.py $experiment
+        rm $outputfile $errorfile & pip install . && bsub -J $name -q bio -n 12 -M 32G -R "rusage[mem=32G]" -o $outputfile -e $errorfile mpiexec -np 5 /home/meerhofj/.conda/envs/fedxgboost_mpi/bin/python tests/main.py $experiment
         ;;
-    "2")
-        rm $outputfile $errorfile & pip install . && bsub -J $name -q bio -n 12 -M 128G -R "rusage[mem=128G]" -o $outputfile -e $errorfile mpiexec -np 4 /home/meerhofj/.conda/envs/fedxgboost_mpi/bin/python tests/main.py $experiment
+    "2" | "3")
+        rm $outputfile $errorfile & pip install . && bsub -J $name -q bio -n 26 -M 128G -R "rusage[mem=128G]" -o $outputfile -e $errorfile mpiexec -np 3 /home/meerhofj/.conda/envs/fedxgboost_mpi/bin/python tests/main.py $experiment
         ;;
     *)  
         echo "only experiment 1 and 2 exist!"
diff --git a/src/SFXGBoost/MemberShip.py b/src/SFXGBoost/MemberShip.py
index b6a7ec0..a25ab06 100644
--- a/src/SFXGBoost/MemberShip.py
+++ b/src/SFXGBoost/MemberShip.py
@@ -44,11 +44,11 @@ def f_random(D_Train_Shadow, D_Out_Shadow, seed=0, take=0):
     X_Train_Shadow = D_Train_Shadow[0][:min_lenght, :]
     X_Out_Shadow = D_Out_Shadow[0][:min_lenght, :]
     if take != 0:
-        take = take//2
+        take = take//2 # TODO investigate centralised!
         if seed != 0:
             np.random.seed(int(seed))
-        take = min(take, min_lenght)  # don't take more than you can
-        indices = np.random.choice(min_lenght, int(take), replace=True)
+        take = min(take, min_lenght) # don't take more than you can
+        indices = np.random.choice(min_lenght, int(take), replace=False)
         X_Train_Shadow = X_Train_Shadow[indices]
         X_Out_Shadow = X_Out_Shadow[indices]
 
@@ -70,10 +70,15 @@ def f_random(D_Train_Shadow, D_Out_Shadow, seed=0, take=0):
     # print(f"X_Train_Attack = {X_Train_Attack.shape}")
     return X_Train_Attack, labels
 
-def create_D_attack_centralised(shadow_model_s, D_Train_Shadow, D_Out_Shadow):
+def create_D_attack_centralised(shadow_model_s, D_Train_Shadow, D_Out_Shadow, config:Config=None):
     # Shadow_model_s can be multiple shadow_models! TODO deal with that!
     x, labels = f_random(D_Train_Shadow, D_Out_Shadow)
-    z = shadow_model_s.predict_proba(x)
+    z = None
+    if type(shadow_model_s) == SFXGBoost:
+        print(f"target rank = {config.target_rank}")
+        z = shadow_model_s.predict_proba(x, config.target_rank)
+    else:
+        z = shadow_model_s.predict_proba(x)
     # z_top_indices = np.argsort(z)[::-1][:3] # take top 3 sorted by probability
     # z = np.take(z, z_top_indices) # take top 3
     return z, labels
@@ -84,7 +89,7 @@ def get_G_H(config:Config, node):
     else:
         return flatten_list([node.G, node.H])
 
-def get_info_node(config:Config, x, node=None):
+def get_info_node(config:Config, x:np.array, node=None):
     # for node in nodes:
     splittinginfo = []
     parent:FLTreeNode = node.parent
@@ -116,31 +121,33 @@ def get_train_Attackmodel_1(config:Config, logger:Logger, n_shadows, c, d, D_Tra
     input = None
     D_Train_Attack = None  # c, l empty lists size is nshadows * (len(D_train_shadow)*2) * n_nodes # where n_nodes scales exponentially
     y=[]
-    max_lenght = 50_000 # try to devide the max_lenght over the different nodes over the available trees. 
-    max_lenght_shadow = max_lenght/n_shadows
-    max_length_shadow_tree = max_lenght_shadow / config.max_tree
+    max_length = 50_000 # try to devide the max_lenght over the different nodes over the available trees. 
+    max_length_shadow = max_length//n_shadows
 
     #TODO take randomly from t, node, then add all NO DUPES x's
     #     
     for a in range(n_shadows):
+        logger.warning(f"attack1 creation shadow model {a} out of {n_shadows} for c={c}, d={d}")
         shadow_model:SFXGBoost = retrieve("shadow_model_" + str(a), config)
-        nodes = shadow_model.nodes[c][d]
+        nodes = np.array(shadow_model.nodes[c][d])  # can be zero
+        if len(nodes) == 0:
+            max_length_shadow += max_length_shadow//(n_shadows-a) # devide the nodes to be put here over the other nodes of other shadow models
+            continue
+        x_len = len(D_Train_Shadow[a][0]) + len(D_Train_Shadow[(a+2)%len(D_Train_Shadow)][0])
+        
+        wish_to_take = int(np.min((max_length_shadow, x_len, len(nodes))))
+        
+        # TODO take 1, but make it unique such that x is not used twice, not vital but could skew results a tiny bit. 
 
+        x, labels_train = f_random(D_Train_Shadow[a], D_Train_Shadow[(a+2) % len(D_Train_Shadow)], seed=a+c+d, take=wish_to_take) #int(max_length_shadow_tree_node)
         
-        indices = np.random.choice(int(len(nodes)), int(np.min((max_length_shadow_tree, len(nodes)))), replace=True)
+        indices = np.random.choice(int(len(nodes)), len(x), replace=False)
         random_nodes = nodes[indices]
-        
-        max_length_shadow_tree_node = None
-        if len(random_nodes) < max_length_shadow_tree:
-            max_length_shadow_tree_node = max_length_shadow_tree // len(random_nodes)
-        else:
-            max_length_shadow_tree_node = 1
 
         for nodepos, node in enumerate(random_nodes):
-            x, labels_train = f_random(D_Train_Shadow[a], D_Train_Shadow[(a+2) % len(D_Train_Shadow)], seed=nodepos+a+c+d, take=max_length_shadow_tree_node)  #int(max_length_shadow_tree_node)
             # z = shadow_model.predict_proba(x)
-            input = get_info_node(config, x, node)
-            y.extend(labels_train)
+            input = get_info_node(config, np.array([x[nodepos]]), node)  # take one of the x's linked to the node, and get the G,H
+            y.append(labels_train[nodepos])  # append would be nicer, 
 
             if not D_Train_Attack is None: 
                 D_Train_Attack = np.vstack((D_Train_Attack, input))
@@ -148,26 +155,6 @@ def get_train_Attackmodel_1(config:Config, logger:Logger, n_shadows, c, d, D_Tra
                 D_Train_Attack = input
     return D_Train_Attack, np.array(y)  # .reshape(-1, 1)
 
-def get_input_attack2dot1(config:Config, logger:Logger, D_train_shadow, models, attack_models):
-    
-
-    input_attack_2 = []
-    y_attack_2 = []
-    nModels = len(models)
-
-    D_train_shadow_offset = [D_train_shadow[a+1] for a in range(nModels)]
-    for c in config.nClasses:
-        for d in config.max_depth:
-            x_attack_1_cd, y_attack_1_cd = get_train_Attackmodel_1(config, logger, models, c, d, D_train_shadow_offset)
-            y_pred = attack_models[c][d].predict_proba(x_attack_1_cd)
-            if input_attack_2 == []:
-                input_attack_2 = y_pred
-            else:
-                input_attack_2 = np.column_stack((input_attack_2, y_pred))
-            y_attack_2.append(y_attack_1_cd)
-    return input_attack_2, y_attack_2
-
-
 def get_input_attack2(config:Config, D_Train_Shadow, n_models, attack_models):            
     input_attack_2 = []
     y_attack_2 = []
@@ -192,17 +179,20 @@ def get_input_attack2(config:Config, D_Train_Shadow, n_models, attack_models):
         for c in range(config.nClasses):
             for d in range(config.max_depth):
                 all_p = None
-                for t in range(config.max_tree):
-                    nodes = model.trees[c][t].root.get_nodes_depth(d)
-                    if nodes == []:
-                        continue
-                    for node in nodes:
-                        node_params = get_info_node(config, x ,node) #size = 2000
-                        list_of_outputs = attack_models[c][d].predict_proba(node_params)[:, 1] # returns two dimentional array!!!!
-                        if all_p is None:
-                            all_p = list_of_outputs
-                        else:
-                            all_p = np.column_stack((all_p, list_of_outputs)) # all probas for every x
+                # nodes = model.trees[c][t].root.get_nodes_depth(d)
+                nodes = model.nodes[c][d]
+                if nodes == []:
+                    continue
+                for node in nodes:
+                    
+                    node_params = get_info_node(config, x ,node) #size = 2000
+                    list_of_outputs = attack_models[c][d].predict_proba(node_params)[:, 1] # returns two dimentional array!!!!
+                    
+                    if all_p is None:
+                        all_p = list_of_outputs
+                    else:
+                        all_p = np.column_stack((all_p, list_of_outputs)) # all probas for every x
+                
                 if all_p is None:
                     print(f"Warning, at depth {d} of model {a} no nodes were found accross all trees of class {c}?!")
                     if (input is None):
@@ -210,9 +200,6 @@ def get_input_attack2(config:Config, D_Train_Shadow, n_models, attack_models):
                     else:
                         input = np.column_stack((input, np.zeros( (x.shape[0],1) )))
                 else:
-                    # print("----")
-                    # print( all_p ) 
-                    # print("----")
                     avg = np.average(all_p, axis=1).reshape((-1,1))
                     # min = np.min(all_p, axis=1).reshape((-1,1))
                     # max = np.max(all_p, axis=1).reshape((-1,1))
diff --git a/src/SFXGBoost/Model.py b/src/SFXGBoost/Model.py
index c520b5c..ad5795f 100644
--- a/src/SFXGBoost/Model.py
+++ b/src/SFXGBoost/Model.py
@@ -123,7 +123,7 @@ def boost(self, init_Probas):
                 # print("#####################")
 
                 nodes = [[self.trees[c][t].root] for c in range(self.config.nClasses)] 
-                for l in range(self.config.max_depth+1):
+                for d in range(self.config.max_depth):
                     G = [[ [] for _ in range(len(nodes[c])) ] for c in range(self.config.nClasses)]
                     H = [[ [] for _ in range(len(nodes[c])) ] for c in range(self.config.nClasses)]
                     for Pi in range(1, comm.Get_size()):
@@ -162,12 +162,12 @@ def boost(self, init_Probas):
                         #     results = list(executor.map(test, range(len(nodes[c]))))
                         # print(results)
                         for i, n in enumerate(nodes[c]): #TODO multithread!
-                            split_cn = self.find_split(splits, G[c][i], H[c][i], l == self.config.max_depth)
+                            split_cn = self.find_split(splits, G[c][i], H[c][i], d == self.config.max_depth)
                             splittingInfos[c].append(split_cn)
                     
                     for Pi in range(1, comm.Get_size()):
                         comm.send(splittingInfos, Pi, tag=MSG_ID.SPLIT_UPDATE )
-                    nodes = self.update_trees(nodes, splittingInfos, l)
+                    nodes = self.update_trees(nodes, splittingInfos, d)
                     # update own my own trees to attack users
         
         ####################
@@ -203,7 +203,7 @@ def boost(self, init_Probas):
                 G, H = np.array(G).T, np.array(H).T  # (nClasses, nUsers)
                 nodes = [[self.trees[c][t].root] for c in range(self.config.nClasses)]
                 
-                for l in range(self.config.max_depth+1):
+                for d in range(self.config.max_depth):
                     Gnodes = [[] for _ in range(self.config.nClasses)]
                     Hnodes = [[] for _ in range(self.config.nClasses)]
 
@@ -220,7 +220,7 @@ def boost(self, init_Probas):
                     self.logger.warning("sending G,H")
                     comm.send((Gnodes,Hnodes), PARTY_ID.SERVER, tag=MSG_ID.RESPONSE_GRADIENTS)
                     splits = comm.recv(source=PARTY_ID.SERVER, tag=MSG_ID.SPLIT_UPDATE)
-                    nodes = self.update_trees(nodes, splits, l) # also update Instances
+                    nodes = self.update_trees(nodes, splits, d) # also update Instances
                 # for c in range(self.config.nClasses):
                 #     FLVisNode(self.logger, self.trees[c][t].root).display(t)
                 update_pred = np.array([tree.predict(orgData) for tree in self.trees[:, t]]).T
@@ -345,13 +345,19 @@ def update_trees(self, last_level_nodes:List[List[FLTreeNode]], splits:List[List
                         node.rightBranch.instances = np.logical_and([self.original_data.featureDict[fName][index] > sValue for index in range(self.nUsers)], node.instances)
                     new_nodes[c].append(node.leftBranch)
                     new_nodes[c].append(node.rightBranch)
-                    self.nodes[c][depth].append(node.leftBranch)
-                    self.nodes[c][depth].append(node.rightBranch)
-                else:
+                else:  # leaf node
                     node.weight = splitInfo.weight
+                    for p in range(0, comm.Get_size() -1):
+                        if rank == PARTY_ID.SERVER:
+                            # print(f"DEBUG gpi shape{np.array(node.Gpi[p]).shape}")
+                            node.weightpi[p], scorep = FLTreeNode.compute_leaf_param(node.Gpi[p][0], node.Hpi[p][0], self.config.lam, self.config.alpha)
+                            node.weightpi[p] = node.weightpi[p] * self.config.learning_rate
                     node.score = splitInfo.nodeScore
                     node.leftBranch = None
                     node.rightBranch = None
+
+                self.nodes[c][depth].append(node)
+
         return new_nodes
 
     def appendGradients(self, instances, G, H, orgData):
@@ -394,7 +400,7 @@ def appendGradients(self, instances, G, H, orgData):
 
     
     def fit(self, X_train, y_train, fName):
-            quantile = QuantiledDataBase(DataBase.data_matrix_to_database(X_train, fName) )
+            quantile = QuantiledDataBase(DataBase.data_matrix_to_database(X_train, fName), self.config ) ## DEL CONFIG if errors occur 12-oct
                 
             initprobability = (sum(y_train))/len(y_train)
             total_users = comm.Get_size() - 1
@@ -432,17 +438,17 @@ def fit(self, X_train, y_train, fName):
             self.boost(initprobability)
             return self
     
-    def predict(self, X): # returns class number
+    def predict(self, X, pi=-1): # returns class number
         """returns one dimentional array of multi-class predictions
 
         Returns:
             _type_: _description_
         """
-        y_pred = self.predict_proba(X) # get leaf node weights
+        y_pred = self.predict_proba(X, pi) # get leaf node weights
         return np.argmax(y_pred, axis=1)
     
-    def predict_proba(self, X): # returns probabilities of all classes
-        y_pred = self.predictweights(X) # returns weights
+    def predict_proba(self, X, pi=-1): # returns probabilities of all classes
+        y_pred = self.predictweights(X, pi) # returns weights
         for rowid in range(np.shape(y_pred)[0]):
             row = y_pred[rowid, :]
             wmax = max(row)
@@ -451,7 +457,7 @@ def predict_proba(self, X): # returns probabilities of all classes
             y_pred[rowid, :] = np.exp(row-wmax) / wsum
         return y_pred
     
-    def predictweights(self, X): # returns weights
+    def predictweights(self, X, pi=-1): # returns weights
             # if type(X) == np.ndarray:
             #     X = DataBase.data_matrix_to_database(X, self.fName)
 
@@ -471,7 +477,7 @@ def predictweights(self, X): # returns weights
                     # self.logger.warning(f"PREDICTION id {treeID}")
                     # b = FLVisNode(self.logger, tree.root)
                     # b.display(treeID)
-                    update_pred = tree.predict(testDataBase)
+                    update_pred = tree.predict(testDataBase, pi)
                     y_pred[:, c] += update_pred #* self.config.learning_rate
             return y_pred
 
@@ -491,7 +497,7 @@ def setInstances(self, nUsers, instances):
         self.root.nUsers = nUsers
         self.root.instances = instances
 
-    def predict(self, data:DataBase):
+    def predict(self, data:DataBase, pi=-1):
         curNode = self.root
         outputs = np.empty(data.nUsers, dtype=float)
 
@@ -505,4 +511,7 @@ def predict(self, data:DataBase):
                 elif(direction == Direction.RIGHT):
                     curNode = curNode.rightBranch
             outputs[userId] = curNode.weight
+            if pi > 0:  # 0 == server, > 0 == participants
+                outputs[userId] = curNode.weightpi[pi-1]
+            # TODO add weightpi return
         return outputs
\ No newline at end of file
diff --git a/src/SFXGBoost/data_structure/databasestructure.py b/src/SFXGBoost/data_structure/databasestructure.py
index c8894ae..2ed432e 100644
--- a/src/SFXGBoost/data_structure/databasestructure.py
+++ b/src/SFXGBoost/data_structure/databasestructure.py
@@ -4,7 +4,7 @@
 
 from SFXGBoost.common.BasicTypes import Direction
 from SFXGBoost.data_structure.treestructure import SplittingInfo
-from SFXGBoost.config import rank
+from SFXGBoost.config import Config, rank
 
 class QuantileParam:
     epsilon = 0.05 # privacy parameter
@@ -17,14 +17,14 @@ def __init__(self, name, dataVector) -> None:
 
 class QuantiledFeature(FeatureData):
     
-    def __init__(self, name, dataVector, splittingMatrix = None, splittingCandidates = None) -> None:
+    def __init__(self, name, dataVector, splittingMatrix = None, splittingCandidates = None, config:Config = None) -> None:
         super().__init__(name, dataVector)
         if (splittingCandidates is None):
-            self.splittingMatrix, self.splittingCandidates = QuantiledFeature.quantile(self.data, QuantileParam) 
+            self.splittingMatrix, self.splittingCandidates = QuantiledFeature.quantile(self.data, config) 
         else:
             self.splittingMatrix, self.splittingCandidates = splittingMatrix, splittingCandidates
 
-    def quantile(fData: FeatureData, param: QuantileParam):
+    def quantile(fData: FeatureData, config:Config = None):
         splittingMatrix = []
         splittingCandidates = []
         # from ddsketch import DDSketch
@@ -32,16 +32,19 @@ def quantile(fData: FeatureData, param: QuantileParam):
 
         # for v in fData:
         #     sketch.add(v)
+        nBuckets = 100
+        if config != None:
+            nBuckets = config.nBuckets
 
-        if len(np.unique(fData)) > 101:
+        if len(np.unique(fData)) > nBuckets+1:
             pass
-            range_min = np.min(fData)
-            range_max = np.max(fData)
+            # range_min = np.min(fData)
+            # range_max = np.max(fData)
             from ddsketch import DDSketch
             sketch = DDSketch()
             for value in fData:
                 sketch.add(value)
-            quantiles = np.array([sketch.get_quantile_value(q/100) for q in range(0, 100, 1)])
+            quantiles = np.array([sketch.get_quantile_value(q/nBuckets) for q in range(0, nBuckets, 1)])
 
             # print(f"DEBUG: {quantiles}")
             splittingCandidates = quantiles
@@ -199,19 +202,20 @@ def __init__(self, dataBase:DataBase = None) -> None:
 
 
 class QuantiledDataBase(DataBase):
-    def __init__(self, dataBase:DataBase = None) -> None:
+    def __init__(self, dataBase:DataBase = None, config:Config = None) -> None:
         super().__init__()
         self.nUsers = dataBase.nUsers
+        self.config = config
         # Perform the quantiled for all the feature (copy, don't change the orgiginal data)
         if dataBase is not None:
             for fName, fData in dataBase.featureDict.items():
-                self.featureDict[fName] = QuantiledFeature(fName, fData)
+                self.featureDict[fName] = QuantiledFeature(fName, fData, config=self.config)
 
         self.gradVec = []
         self.hessVec = []
     
     def splitupHorizontal(self, start:int, end:int):
-        newQDatabase = QuantiledDataBase(DataBase())
+        newQDatabase = QuantiledDataBase(DataBase(), self.config)
         newQDatabase.nUsers = end - start
         newQDatabase.featureDict = {}
         # self.splittingMatrix, self.splittingCandidates = QuantiledFeature.quantile(self.data, QuantileParam)     
@@ -219,9 +223,9 @@ def splitupHorizontal(self, start:int, end:int):
         for featurestring, qFeature in self.featureDict.items():
             newdata = qFeature.data[start:end]
             if qFeature.splittingCandidates.size != 0:
-                newQDatabase.featureDict[featurestring] = QuantiledFeature(name=featurestring, dataVector=newdata, splittingCandidates=qFeature.splittingCandidates)
+                newQDatabase.featureDict[featurestring] = QuantiledFeature(name=featurestring, dataVector=newdata, splittingCandidates=qFeature.splittingCandidates, config=self.config)
             else: # empty
-                newQDatabase.featureDict[featurestring] = QuantiledFeature(name=featurestring, dataVector=newdata, splittingCandidates=qFeature.splittingCandidates) 
+                newQDatabase.featureDict[featurestring] = QuantiledFeature(name=featurestring, dataVector=newdata, splittingCandidates=qFeature.splittingCandidates, config=self.config) 
         return newQDatabase
     
     def get_info_string(self):
diff --git a/src/SFXGBoost/data_structure/treestructure.py b/src/SFXGBoost/data_structure/treestructure.py
index 0bd249f..3878589 100644
--- a/src/SFXGBoost/data_structure/treestructure.py
+++ b/src/SFXGBoost/data_structure/treestructure.py
@@ -52,6 +52,7 @@ def __init__(self, weight = 0.0, leftBranch=None, rightBranch=None):
         self.weight = weight
         self.leftBranch = leftBranch
         self.rightBranch = rightBranch
+        self.weightpi = [ 0 for _ in range(comm.Get_size() -1)]
         
 
     def logNode(self, logger):
@@ -89,7 +90,7 @@ def __init__(self, FID = 0, weight=0, parent=None, leftBranch=None, rightBranch=
         self.Hpi = [ [] for _ in range(comm.Get_size() - 1)]
         self.splitting_sgh = None  # NOT USED YET
         self.splitting_sgh = None  # store parent's splits, + your gradients
-        
+
     def get_nodes_depth(self, l, cur_l=0):
         """retrieves all the nodes that are at depth l [0, max_depth]
 
diff --git a/src/SFXGBoost/dataset/datasetRetrieval.py b/src/SFXGBoost/dataset/datasetRetrieval.py
index b63b907..d0d761e 100644
--- a/src/SFXGBoost/dataset/datasetRetrieval.py
+++ b/src/SFXGBoost/dataset/datasetRetrieval.py
@@ -7,6 +7,9 @@
 dataset_list = ['purchase-10', 'purchase-20', 'purchase-50', 'purchase-100', 'texas', 'MNIST', 'synthetic', 'Census', 'DNA']
 
 def apply_DP(X, y):
+    import pydp
+    pydp.algorithms.laplacian.BoundedStandardDeviation
+    pydp.LaplacePrivacy({'epsilon':epsilon})
     sensitivity = 1
     epsilon = 1
     scale = sensitivity/epsilon
@@ -240,10 +243,14 @@ def getHealthcare(paths, federated=False, train_size=2_000): # https://www.kaggl
         _type_: _description_
     """
     
-    n_shadows = 100
+    n_shadows = 300
     # A MINIMUM OF 4 SHADOWS ARE NEEDED!!!
     def returnfunc():
         train = check_mul_paths_csv("AV_HealthcareAnalyticsII/train_data", paths)
+        # train_dp = check_mul_paths_csv("AV_HealthcareAnalyticsII/train_data_dp", paths)
+
+        # x = pd.read_csv('/data/BioGrid/meerhofj/Database/AV_HealthcareAnalyticsII/train_data_dp.tsv', sep='\t')
+
         # test = check_mul_paths_csv("AV_HealthcareAnalyticsII/test_data", paths)
         # dict = check_mul_paths_csv("AV_HealthcareAnalyticsII/train_data_dictionary", paths)
         # sample = check_mul_paths_csv("AV_HealthcareAnalyticsII/sample_sub", paths)
@@ -308,4 +315,6 @@ def getConfigParams(dataBaseName): # retreive n_classes, n_features
                         'Census':(-1, -1), 
                         'DNA':(-1, -1)
                    }[dataBaseName]
-    return get_databasefunc[0], get_databasefunc[1]
\ No newline at end of file
+    return get_databasefunc[0], get_databasefunc[1]
+
+# x = 1
\ No newline at end of file
diff --git a/src/SFXGBoost/view/plotter.py b/src/SFXGBoost/view/plotter.py
index 684d7f6..2c463be 100644
--- a/src/SFXGBoost/view/plotter.py
+++ b/src/SFXGBoost/view/plotter.py
@@ -73,12 +73,12 @@ def plot_histogram(datasets, data, title="Sample text", y_label="y_label", desti
     day = date.today().strftime("%b-%d-%Y")
     curTime = time.strftime("%H:%M", time.localtime())
     destination = destination.replace(".jpeg", f"{day},{curTime}.jpeg")
-
     width = 0.25
     multiplier = 0
     x = np.arange(len(datasets))
     fig, ax = plt.subplots(layout='constrained')
     for attribute, measurements in data.items():
+        measurements = [round(value, 3,) for value in measurements]
         offset = width * multiplier
         rects = ax.bar(x+offset, measurements, width, label=attribute)
         ax.bar_label(rects, padding=3)
diff --git a/src/SFXGBoost/view/table.py b/src/SFXGBoost/view/table.py
index de55fa6..881b9a6 100644
--- a/src/SFXGBoost/view/table.py
+++ b/src/SFXGBoost/view/table.py
@@ -5,7 +5,7 @@ def create_latex_table_tmp(h_axis, data, name):
 
     latex_table = "\\begin{table}[h]\n"
     latex_table += "\\centering\n"
-    latex_table += "\\begin{tabular}{| "+ "|".join(["c" for _ in h_axis]) + "|}\n\\hline"
+    latex_table += "\\begin{tabular}{|"+ "|".join(["c" for _ in h_axis]) + "|}\n\\hline"
     latex_table += "\n" + " & ".join(h_axis) + "\\\\\n\\hline\n"
 
     for key, value in data.items():
@@ -18,6 +18,8 @@ def create_latex_table_tmp(h_axis, data, name):
 
     output_filename = name + ".tex"
     
+    output_filename = output_filename.replace("_", "\_")
+
     with open(output_filename, 'w') as f:
         f.write(latex_table)
 
@@ -33,16 +35,6 @@ def create_table_config_variable(name, modeltype, *args, **kwargs):
     data["model"] = modeltype
     create_latex_table_tmp(h_axis, data, "./Table/config_" + name)
     return
-    if modeltype(model) is xgb.XGBClassifier:
-        pass
-        xgb.XGBClassifier.get_params
-    elif modeltype(model) is MLPClassifier:
-        data = kwargs
-        create_latex_table_tmp(h_axis, data, "./Table/config_" + name)
-    elif modeltype(model) is SFXGBoost:
-        # create_table_config(kwargs["alpha"],kwargs["gamma"], kwargs["lam"], kwargs["learning_rate"], kwargs["max_depth"], kwargs["max_tree"], )
-        data = kwargs
-        create_latex_table_tmp(h_axis, data, "./Table/config_" + name)
     
 def create_latex_table_1(all_data, to_be_tested, metrics, name_model, datasets, destination="Table/experiment_1_.txt"):
     
diff --git a/tests/main.py b/tests/main.py
index 32fd52e..823899b 100644
--- a/tests/main.py
+++ b/tests/main.py
@@ -29,7 +29,7 @@ def log_distribution(logger, X_train, y_train, y_test):
     nTrain, rTrain, nTest, rTest, X_train.shape[1])
 
 def fit(X_train, y_train, X_test, fName, model):
-    quantile = QuantiledDataBase(DataBase.data_matrix_to_database(X_train, fName) )
+    quantile = QuantiledDataBase(DataBase.data_matrix_to_database(X_train, fName))
 
     initprobability = (sum(y_train))/len(y_train)
     total_users = comm.Get_size() - 1
@@ -93,7 +93,12 @@ def test_global(config:Config, logger:Logger, model: SFXGBoostClassifierBase, ge
                       "/home/hacker/jaap_cloud/SchoolCloud/Master Thesis/Database/", \
                       "/home/jaap/Documents/JaapCloud/SchoolCloud/Master Thesis/Database/"]
 
-def get_data_attack_centrally(target_model, shadow_model, attack_model, config, logger:Logger, name="sample"):
+def get_data_attack_federated2(target_model, shadow_model, attack_model, config:Config, logger:Logger):
+    X_train, y_train, X_test, y_test, fName, X_shadow, y_shadow = getDataBase(config.dataset, POSSIBLE_PATHS, False, config.train_size)()
+    log_distribution(logger, X_train, y_train, y_test)
+    
+
+def get_data_attack_centrally(target_model, shadow_model, attack_model, config:Config, logger:Logger, name="sample"):
     # TODO keep track of rank 
     X_train, y_train, X_test, y_test, fName, X_shadow, y_shadow = getDataBase(config.dataset, POSSIBLE_PATHS, False, config.train_size)()
     log_distribution(logger, X_train, y_train, y_test)
@@ -104,11 +109,20 @@ def get_data_attack_centrally(target_model, shadow_model, attack_model, config,
     else:
         target_model.fit(X_train, np.argmax(y_train, axis=1))
         shadow_model.fit(D_Train_Shadow[0], np.argmax(D_Train_Shadow[1], axis=1))
-    z, labels = create_D_attack_centralised(shadow_model, D_Train_Shadow, D_Out_Shadow)
     data = None
     if rank == PARTY_ID.SERVER:
+        print(f"DEBUG: target rank = {config.target_rank}, name = {name}")
+        if config.target_rank > 0 and name == "FederBoost-Federated-2" :
+            pickle.dump(shadow_model, open("./delme.p", 'wb'))
+            print("went intside if")
+            X_train, y_train = devide_D_Train(X_train, y_train, config.target_rank, config.data_devision)
+            D_Train_Shadow = devide_D_Train(D_Train_Shadow[0], D_Train_Shadow[1], config.target_rank, config.data_devision)
+            X_test, y_test = devide_D_Train(X_test, y_test, config.target_rank, config.data_devision)
+
+        z, labels = create_D_attack_centralised(shadow_model, D_Train_Shadow, D_Out_Shadow, config)
+
         attack_model.fit(z, labels)
-        z_test, labels_test = create_D_attack_centralised(target_model, (X_train, y_train), (X_test, y_test))
+        z_test, labels_test = create_D_attack_centralised(target_model, (X_train, y_train), (X_test, y_test), config)
         
         data = retrieve_data(target_model, shadow_model, attack_model, X_train, y_train, X_test, y_test, z_test, labels_test)
         logger.warning("accuracy test attack: " + str(data["accuracy test attack"]))
@@ -146,6 +160,10 @@ def concurrent_multi(c, config, logger, shadow_models, D_Train_Shadow, attack_mo
             D_Train_Attack, y = get_train_Attackmodel_1(config, shadow_models, c, l, D_Train_Shadow) # TODO this is too slow, multithread?
         attack_models[c][l] = retrieveorfit(attack_models[c][l], f"Attack_model {c},{l}", config, D_Train_Attack, np.array(y))
 
+def use_federated2(target_model, shadow_model, attack_model, config, logger) -> dict:
+    X_train, y_train, X_test, y_test, fName, X_shadow, y_shadow = getDataBase(config.dataset, POSSIBLE_PATHS, True, config.train_size)()
+
+
 def train_all_federated(target_model, shadow_models, attack_models1:List, attack_model2, config:Config, logger:Logger) -> dict:
     """Trains for a federated attack given the target_model, shadow_model(s), attack_model, config, logger
 
@@ -193,7 +211,7 @@ def train_all_federated(target_model, shadow_models, attack_models1:List, attack
             X_train, y_train = devide_D_Train(X_train, y_train, config.target_rank, config.data_devision)  # change D_target to be the same as actually used by the targeted user
             X_test, y_test = devide_D_Train(X_test, y_test, config.target_rank, config.data_devision)  # change D_test to be same size as targeted user
         
-        with ThreadPoolExecutor(max_workers=10) as executor:
+        with ThreadPoolExecutor(max_workers=11) as executor:
             future_D_Train_Attack = [executor.submit(concurrent, config, logger, c, n_shadows, deepcopy(D_Train_Shadow), deepcopy(attack_models1)) for c in range(config.nClasses)  ]
 
             for future in futures.as_completed(future_D_Train_Attack):
@@ -295,6 +313,7 @@ def retrieve_data(target_model, shadow_model, attack_model, X_train, y_train, X_
     data["recall test attack"] = recall_test_attack
     return data
 
+
 def create_attack_model_federated(config:Config, G:list) -> list:
     """_summary_
 
@@ -339,19 +358,21 @@ def experiment1():
 
     # datasets = ["synthetic-10", "synthetic-20", "synthetic-50", "synthetic-100"]
 
-    # targetArchitectures = ["XGBoost", "FederBoost-central"]
-    targetArchitectures = ["FederBoost-central"]
+    # targetArchitectures = ["XGBoost", "FederBoost-Central"]
+    targetArchitectures = ["FederBoost-Central"]
 
     all_data = {} # all_data["XGBoost"]["healthcore"]["accuarcy train shadow"]
     # tested_param = "gamma"
     # tested_param_vals = [0.1, 0.5, 0.7, 0.9, 1]
-    to_be_tested = {"gamma": [0, 0.1, 0.25, 0.5, 0.75, 1, 5, 10], # [0,inf] minimum loss for split to happen default = 0
-                    "max_depth": [5, 8, 12, 15],
-                    "max_tree": [5, 10, 20, 30, 50, 100, 150],
-                    # "training_size": [1000, 2000, 5000, 10_000, 30_000],
-                    "alpha": [0, 0.1, 0.25, 0.5, 0.75, 1, 10],  # [0, inf] L1 regularisation default = 0
-                    "lam":   [0, 0.1, 0.25, 0.5, 0.75, 1, 10],  # L2 regularisation [0, inf] default = 1
-                    "learning_rate":   [0.1, 0.25, 0.5 ,0.75, 1]  # learning rate [0,1] default = 0.3
+    to_be_tested = {
+                    # "max_tree": [5, 10, 20, 30, 50, 100, 150],
+                    # "max_depth": [5, 8, 12, 15],
+                    # "train_size": [1000, 2000, 5000, 10_000],  # learning rate [0,1] default = 0.3
+                    # "gamma": [0, 0.1, 0.25, 0.5, 0.75, 1, 5, 10], # [0,inf] minimum loss for split to happen default = 0
+                    # "alpha": [0, 0.1, 0.25, 0.5, 0.75, 1, 10],  # [0, inf] L1 regularisation default = 0
+                    # "lam":   [0, 0.1, 0.25, 0.5, 0.75, 1, 10],  # L2 regularisation [0, inf] default = 1
+                    # "learning_rate":   [0.1, 0.25, 0.5 ,0.75, 1],
+                    "nBuckets": [10, 20, 50, 100, 1000]
                     }
 
     # to_be_tested = {"gamma": [0, 0.1],
@@ -378,10 +399,10 @@ def experiment1():
                             learning_rate=0.3,
                             max_depth=8,
                             max_tree=20,
-                            nBuckets=35,
+                            nBuckets=100,
                             save=False,
                             data_devision=[0.25, 0.25, 0.25, 0.25],
-                            train_size=25_000)
+                            train_size=10_000)
                     create_table_config_variable("experiment1_targetandshadow", modeltype="FederBoost",
                                                  alpha=config.alpha, gamma=config.gamma, 
                                                  lam=config.lam, 
@@ -438,7 +459,14 @@ def experiment2dot1():
         for dataset in datasets:
             pass
 
-def experiment2():
+def experiment3():
+    datasets = ["healthcare"]
+    targetArchitectures = ["XGBoost", "FederBoost-Central", "FederBoost-Federated-2"]
+    all_data = {} # all_data["XGBoost"]["healthcore"]["accuarcy train shadow"]
+
+
+
+def experiment2_3(experiment_number:int=2):
     seed = 10
     # datasets = ["healthcare", "MNIST", "synthetic", "Census", "DNA", "Purchase-10", "Purhase-20", "Purchase-50", "Purchase-100"]
     # datasets = ["synthetic"]
@@ -446,39 +474,43 @@ def experiment2():
 
     # datasets = ["healthcare", "synthetic-10", "synthetic-20", "synthetic-50", "synthetic-100"]
     # datasets = ["synthetic-10", "synthetic-100"]
-    datasets = ["healthcare"]
+    datasets = ["healthcare"]#, "synthetic-10", "synthetic-100"]
 
 
-    targetArchitectures = ["XGBoost", "FederBoost-central", "FederBoost-federated"]
+    targetArchitectures = ["XGBoost", "FederBoost-Central", "FederBoost-Federated-2"] # , "FederBoost-Federated-1"]
     # targetArchitectures = ["FederBoost-federated"]
 
     all_data = {} # all_data["XGBoost"]["healthcore"]["accuarcy train shadow"]
     for targetArchitecture in targetArchitectures:
         all_data[targetArchitecture] = {}
         for dataset in datasets:
-            config = Config(experimentName = "experiment 2",
+            config = Config(experimentName = f"experiment {experiment_number}",
             nameTest= dataset + " test",
             model="normal",
             dataset=dataset,
             lam=0.1, # 0.1 10
-            gamma=0,
+            gamma=0.5,
             alpha=0.0,
             learning_rate=0.3,
-            max_depth=12,
+            max_depth=8,
             max_tree=20,
-            nBuckets=35,
+            nBuckets=100,
             save=True,
             target_rank=1,
-            data_devision=[0.0125, 0.4875, 0.5],
-            train_size=2000)
-            create_table_config_variable("experiment1_targetandshadow", modeltype="FederBoost",
+            # data_devision=[0.0125, 0.4875, 0.5],
+            data_devision=[1000/10_000, 9_000/10_000],
+            train_size=10_000)
+            create_table_config_variable("experiment2_targetandshadow", modeltype="FederBoost",
                                         alpha=config.alpha, gamma=config.gamma, 
                                         lam=config.lam, 
                                         learningrate = config.learning_rate,
                                     maxdepth = config.max_depth, 
-                                    maxtree = config.max_tree, 
+                                    maxtree = config.max_tree,
+                                    data_devision = config.data_devision,
+                                    nBuckets = config.nBuckets,
+                                    train_size = config.train_size 
                             )
-            central_config_attack= {"max_depth":12, "objective":"binary:logistic", "tree_method=":"approx", 
+            central_config_attack= {"max_depth":12, "objective":"binary:logistic", "tree_method":"approx", 
                              "learning_rate":0.3, "n_estimators":20, "gamma":0, "reg_alpha":0.0, "reg_lambda":1}
             
             create_table_config_variable("exp2 central attack", modeltype="XGBClassifier", **central_config_attack)
@@ -493,7 +525,9 @@ def experiment2():
 
             np.random.RandomState(seed) # TODO set seed sklearn.split
             
-            if targetArchitecture == "FederBoost-central":
+            if targetArchitecture == "FederBoost-Central" or targetArchitecture == "FederBoost-Federated-2":
+                if targetArchitecture == "FederBoost-Central":
+                    config.target_rank = 0  # make sure when doing central, the target rank is 0 so total train size is tested against
                 target_model = SFXGBoost(config, logger)
                 shadow_model = SFXGBoost(config, logger)
                 # attack_model = MLPClassifier(hidden_layer_sizes=(20,11,11), activation='relu', solver='adam', learning_rate_init=0.01, max_iter=2000)
@@ -501,10 +535,10 @@ def experiment2():
                 data = get_data_attack_centrally(target_model, shadow_model, attack_model, config, logger, targetArchitecture)
                 if rank == PARTY_ID.SERVER:
                     all_data[targetArchitecture][dataset] = data
-            elif targetArchitecture == "FederBoost-federated":
+            elif targetArchitecture == "FederBoost-Federated-1":
                 target_model = SFXGBoost(config, logger)
 
-                shadow_models = [SFXGBoost(config, logger) for _ in range(100)]  # TODO create the amount of shadow models allowed given by datasetretrieval
+                shadow_models = [SFXGBoost(config, logger) for _ in range(300)]  # TODO create the amount of shadow models allowed given by datasetretrieval
                 
                 # attack_models1 = [ [MLPClassifier(hidden_layer_sizes=(300, 100,50,1), activation='relu', solver='adam', learning_rate_init=0.01, max_iter=2000) for l in range(config.max_depth)] for c in range(config.nClasses)]
                 
@@ -517,6 +551,7 @@ def experiment2():
                 if rank == PARTY_ID.SERVER:
                     all_data[targetArchitecture][dataset] = data
             elif targetArchitecture == "XGBoost" and rank == PARTY_ID.SERVER:  # define neural network
+                config.target_rank = 0
                 target_model = xgb.XGBClassifier(max_depth=config.max_depth, objective="multi:softmax", tree_method="approx", num_class=config.nClasses,
                         learning_rate=config.learning_rate, n_estimators=config.max_tree, gamma=config.gamma, reg_alpha=config.alpha, reg_lambda=config.lam)
                 shadow_model = xgb.XGBClassifier(max_depth=config.max_depth, objective="multi:softmax", tree_method="approx", num_class=config.nClasses,
@@ -540,14 +575,14 @@ def experiment2():
 
 def main():
     if len(sys.argv) < 2:
-        print("Usage: python main.py <exeperiment number>")
+        print("Usage: python main.py <experiment number>")
     else:
         experimentNumber = int(sys.argv[1])
         print(f"running experiment {experimentNumber}!")
         if experimentNumber == 1:
             experiment1()
-        elif experimentNumber == 2:
-            experiment2()
+        elif experimentNumber == 2 or experimentNumber == 3:
+            experiment2_3(experimentNumber)
     if False:
         dataset = "healthcare"
         config = Config(experimentName = "experiment 2",
diff --git a/tests/testing.py b/tests/testing.py
index 69a3c6b..c721d27 100644
--- a/tests/testing.py
+++ b/tests/testing.py
@@ -13,17 +13,18 @@
 from SFXGBoost.common.pickler import save
 from SFXGBoost.view.table import create_latex_table_1
 # var = 9
+gpi = pickle.load(open("Gpi.p", "rb"))
+
 arguments = pickle.load(open("./Saves/arguments.p", "rb"))
 # create_latex_table_1(all_data=arguments[0], to_be_tested=arguments[1], metrics=arguments[2], name_model=arguments[3], datasets=arguments[4], destination="./Table/experiment_1.txt")
-# pickle.dump(var, open("./Saves/kanker.p", 'wb'))
 # 10,5 works really well. 
-# shadow = pickle.load(open('Saves/healthcare test_rank_1/healthcare_shadow_model_0.p', 'rb'))
-D_train = pickle.load(open('/mnt/scratch_dir/meerhofj/Saves/healthcare test_rank_0/healthcare_D_Train_Attack_0,11.p', 'rb'))
+shadow = pickle.load(open('/mnt/scratch_dir/meerhofj/Saves/healthcare test_rank_0/healthcare_shadow_model_1.p', 'rb'))
+D_train = pickle.load(open('/mnt/scratch_dir/meerhofj/Saves/healthcare test_rank_0/healthcare_D_Train_Attack_0,3.p', 'rb'))
 
 # D_test = pickle.load(open('Saves/healthcare test_rank_0/healthcare_D_test_attack2.p', 'rb'))
 
 # tmp = pickle.load(open('Saves/healthcare test_rank_0/healthcare_D_train_attack2.p', 'rb'))
-if True:
+if False:
     x = 1
     D_train = pickle.load(open('/mnt/scratch_dir/meerhofj/Saves/healthcare test_rank_0/healthcare_D_train_attack2.p', 'rb'))
     np.column_stack((D_train[0][7], D_train[0][14], D_train[0][21]))