Type
New Baseline
Description
I would like to contribute a Flower Baseline for SSFL: Discovering Unified Sparse Subnetworks at Initialization for Efficient Federated Learning.
Method
SSFL (Salient Sparse Federated Learning) discovers a unified sparse subnetwork at initialization by aggregating client-local saliency scores. Clients then train within this fixed shared subspace and communicate only active parameters. The paper provides early evidence that learning in a unified sparse subspace can improve the accuracy–sparsity trade-off compared with dynamic pruning methods that continually change the sparse topology during training, while reducing communication relative to dense FL.
Why this is a useful Flower baseline
- Reproduces a published method with a clear, self-contained pipeline .
- Targets a common research need: sparse / communication-efficient FL with non-IID data.
- Fits Flower’s stack well (PyTorch, Flower Datasets, Message API, sparse payload transport).
- Provides paper-matching CIFAR-10/100 configs that others can extend or compare against.
Planned experiments
- CIFAR-10 and CIFAR-100
- ResNet-18
- Static SSFL at density
0.5
- 100 clients, 10% participation per round
- Balanced Dirichlet partitioning with
α = 0.3
- Paper-matching optimizer and training configurations for CIFAR-10 and CIFAR-100
Planned Implementation
- Port the our Flower App into
baselines/ssfl.
- Reproduce SSFL’s saliency-based mask discovery and sparse training using Flower Datasets and the Message API.
- Add paper configurations and documentation for CIFAR-10/100.
Additional Context
I am one of the paper’s authors.
A working Flower App port and paper-scale runs already exist and we are testing to share soon. We plan to contribute the CIFAR-10/100 static SSFL baseline first, then potentially add more experiments later.
The baseline will use PyTorch, Flower Datasets, and the Flower Message API.
Type
New Baseline
Description
I would like to contribute a Flower Baseline for SSFL: Discovering Unified Sparse Subnetworks at Initialization for Efficient Federated Learning.
Method
SSFL (Salient Sparse Federated Learning) discovers a unified sparse subnetwork at initialization by aggregating client-local saliency scores. Clients then train within this fixed shared subspace and communicate only active parameters. The paper provides early evidence that learning in a unified sparse subspace can improve the accuracy–sparsity trade-off compared with dynamic pruning methods that continually change the sparse topology during training, while reducing communication relative to dense FL.
Why this is a useful Flower baseline
Planned experiments
0.5α = 0.3Planned Implementation
baselines/ssfl.Additional Context
I am one of the paper’s authors.
A working Flower App port and paper-scale runs already exist and we are testing to share soon. We plan to contribute the CIFAR-10/100 static SSFL baseline first, then potentially add more experiments later.
The baseline will use PyTorch, Flower Datasets, and the Flower Message API.