Type
New Baseline
Description
New baseline proposal
Paper: Entity Augmentation for Efficient Classification of Vertically Partitioned Data with Limited Overlap
Authors: Avi Amalanshu, Viswesh Nagaswamy, G.V.S.S. Prudhvi, Yash Sirvi, Debashish Chakravarty (IIT Kharagpur)
Link: https://arxiv.org/abs/2406.17899
I am one of the authors of this paper and would like to contribute it as a Flower Baseline.
Summary of the method
Traditional VFL requires entity resolution, private set intersection and entity alignment, so
guests can only train on the intersection of their entity sets and must discard the rest of their
data. Entity Augmentation removes the alignment step entirely: guests forward whichever entities
they hold, in whatever order, and the host synthesises a label for each concatenated activation by
interpolating the labels of the contributing entities, weighted by each guest's activation
dimension. This is functionally a form of CutMix in activation space, so it also acts as a
regulariser. With only 5% entity overlap on CIFAR-10, adding the misaligned data via Entity
Augmentation raises test accuracy from 48.1% to 69.48%.
Experiments to reproduce
Primary scope — CIFAR-10, ResNet-18, 2 guests, 60 epochs, Adam (β1=0.9, β2=0.999), lr=0.001,
with each guest owning a CNN branch plus half of the first fully-connected layer and the host
owning the remaining fully-connected layers:
-
Limited overlap (Table 4). Dataset split into x% entity-aligned data, with the remaining
(100−x)% shuffled and divided evenly between the two guests as strictly non-overlapping
samples. Train on the aligned subset only, versus aligned + misaligned via Entity Augmentation:
| Setting |
Aligned only |
Aligned + Misaligned |
| CIFAR-10, x = 5% |
48.1% |
69.48% |
| CIFAR-10, x = 10% |
54.34% |
70.4% |
-
Full alignment vs full misalignment (Table 2). Train on fully entity-aligned data versus
fully misaligned data with Entity Augmentation — CIFAR-10 / ResNet-18: 72.92% vs 74.34%.
-
Training curves (Figure 2). Test accuracy per epoch for the above settings, showing the
faster convergence and smoother/more stable training that Entity Augmentation produces.
Planned extensions once the CIFAR-10 pipeline is reviewed:
- MNIST with ResNet-18: 97.1% → 98.37% (x = 5%), 97.92% → 98.5% (x = 10%), and 99.25% vs 98.20%
for fully aligned vs misaligned.
- Deeper architectures on both datasets: ResNet-56 (77.09% vs 79.12% on CIFAR-10) and
ResNeXt-29 8x64 (81.08% vs 82.06%).
Implementation notes
- Built on the Message API: the host
ServerApp drives mini-batch VFL rounds through Grid —
a query message collects guest embeddings, a train message delivers the corresponding
embedding gradients.
- Guest model and optimizer state persist across messages via
context.state, following the
pattern established by the reworked examples/vertical-fl.
- Unlike
examples/vertical-fl (full-batch, tabular, entity-aligned), this baseline performs
mini-batch VFL training on images with partial or zero entity overlap. As far as I can
tell it would be the first VFL entry in baselines/.
- Data loading and vertical partitioning through
flwr-datasets; runs with a single flwr run .
and supports --run-config overrides.
Planned Implementation
No response
Additional Context
No response
Type
New Baseline
Description
New baseline proposal
Paper: Entity Augmentation for Efficient Classification of Vertically Partitioned Data with Limited Overlap
Authors: Avi Amalanshu, Viswesh Nagaswamy, G.V.S.S. Prudhvi, Yash Sirvi, Debashish Chakravarty (IIT Kharagpur)
Link: https://arxiv.org/abs/2406.17899
I am one of the authors of this paper and would like to contribute it as a Flower Baseline.
Summary of the method
Traditional VFL requires entity resolution, private set intersection and entity alignment, so
guests can only train on the intersection of their entity sets and must discard the rest of their
data. Entity Augmentation removes the alignment step entirely: guests forward whichever entities
they hold, in whatever order, and the host synthesises a label for each concatenated activation by
interpolating the labels of the contributing entities, weighted by each guest's activation
dimension. This is functionally a form of CutMix in activation space, so it also acts as a
regulariser. With only 5% entity overlap on CIFAR-10, adding the misaligned data via Entity
Augmentation raises test accuracy from 48.1% to 69.48%.
Experiments to reproduce
Primary scope — CIFAR-10, ResNet-18, 2 guests, 60 epochs, Adam (β1=0.9, β2=0.999), lr=0.001,
with each guest owning a CNN branch plus half of the first fully-connected layer and the host
owning the remaining fully-connected layers:
Limited overlap (Table 4). Dataset split into x% entity-aligned data, with the remaining
(100−x)% shuffled and divided evenly between the two guests as strictly non-overlapping
samples. Train on the aligned subset only, versus aligned + misaligned via Entity Augmentation:
Full alignment vs full misalignment (Table 2). Train on fully entity-aligned data versus
fully misaligned data with Entity Augmentation — CIFAR-10 / ResNet-18: 72.92% vs 74.34%.
Training curves (Figure 2). Test accuracy per epoch for the above settings, showing the
faster convergence and smoother/more stable training that Entity Augmentation produces.
Planned extensions once the CIFAR-10 pipeline is reviewed:
for fully aligned vs misaligned.
ResNeXt-29 8x64 (81.08% vs 82.06%).
Implementation notes
ServerAppdrives mini-batch VFL rounds throughGrid—a
querymessage collects guest embeddings, atrainmessage delivers the correspondingembedding gradients.
context.state, following thepattern established by the reworked
examples/vertical-fl.examples/vertical-fl(full-batch, tabular, entity-aligned), this baseline performsmini-batch VFL training on images with partial or zero entity overlap. As far as I can
tell it would be the first VFL entry in
baselines/.flwr-datasets; runs with a singleflwr run .and supports
--run-configoverrides.Planned Implementation
No response
Additional Context
No response