Efficient and Safe Federated Learning!

Federated learning (FL) enables decentralized training across multiple devices while preserving data privacy. However, communication bottlenecks severely hinder its efficiency due to the high-dimensional nature of model updates. While contractive compressors (e.g., Top-$K$) can alleviate this issue, they often degrade performance without proper handling. Safe-EF tackles this challenge by integrating error feedback (EF) with safety constraints, ensuring both efficiency and robustness in federated learning.

• Communication-Efficient Learning: Employs contractive compression (e.g., Top-$K$) to reduce communication costs.
• Error Feedback for Stability: Mitigates the adverse effects of compression to maintain convergence speed and accuracy.
• Safety Constraints: Ensures feasible updates, even in non-smooth convex settings, making it practical for real-world applications.
• Scalability & Robustness: Extends to stochastic settings and large-scale federated learning setups.

• Python • Version 3.10+
• pip • Python package installer

Get started with Safe-EF in just a few steps:

1. Clone the repository:

   git clone https://github.com/anon/safe-ef.git cd safe-ef

2. Create a virtual environment:

   python3 -m venv venv source venv/bin/activate

3. Install dependencies:

   pip install -e .

1. Clone the repository:

   git clone https://github.com/anon/safe-ef.git cd safe-ef

2. Install dependencies and create a virtual environment with Poetry:

   poetry install

3. Activate the virtual environment:

   poetry shell

Run the training script with:

python train_brax.py --help

This will display all available options and configurations.

Check out our ICML paper:

@inproceedings{islamovsafe, title={Safe-EF: Error Feedback for Non-smooth Constrained Optimization}, author={Islamov, Rustem and As, Yarden and Fatkhullin, Ilyas}, booktitle={Forty-second International Conference on Machine Learning} }