Official PyTorch implementation of the paper:
FedBand: Adaptive Federated Learning Under Strict Bandwidth Constraints
Taghreed Alanazi; Abdulrahman Fahim; Muntaka Ibnath; Basak Guler; Amit Roy Chowdhury; Ananthram Swami; Evangelos E. Papalexakis; Srikanth V. Krishnamurthy, ICCCN 2025
https://ieeexplore.ieee.org/document/11133779
FedBand is a communication-efficient Federated Learning (FL) framework that dynamically allocates per-client compression budgets based on model behavior (validation loss or gradient norm).
The method enforces a global bandwidth constraint each round while maximizing accuracy and fairness.
This repository includes:
- CIFAR-10 images classification experiments
- UTMobileNet2021 trafic classification experiments
- Dynamic bandwidth allocation
- Top-k / sparse update compression
- Fairness & per-client metrics
git clone https://github.com/t-alanazi/FedBand.git
cd FedBand
pip install -r requirements.txt
## Run CIFAR-10
python3 -m fedband.run_cifar10
## Run UTMobileNet2021
python3 -m fedband.run_utmobilenet --base_path /path/to/data