CFL-SparseMed: Communication-Efficient Federated Learning for Medical Imaging with Top-k Sparse Updates
Abstract
Secure and reliable medical image classification is crucial for effective patient treatment, but centralized models face challenges due to data and privacy concerns. Federated Learning (FL) enables privacy-preserving collaborations but struggles with heterogeneous, non-IID data and high communication costs, especially in large networks. We propose \textbf{CFL-SparseMed}, an FL approach that uses Top-k Sparsification to reduce communication overhead by transmitting only the top k gradients. This unified solution effectively addresses data heterogeneity while maintaining model accuracy. It enhances FL efficiency, preserves privacy, and improves diagnostic accuracy and patient care in non-IID medical imaging settings. The reproducibility source code is available on \href{https://github.com/Aniket2241/APK_contruct}{Github}.
Cite
@article{arxiv.2510.24776,
title = {CFL-SparseMed: Communication-Efficient Federated Learning for Medical Imaging with Top-k Sparse Updates},
author = {Gousia Habib and Aniket Bhardwaj and Ritvik Sharma and Shoeib Amin Banday and Ishfaq Ahmad Malik},
journal= {arXiv preprint arXiv:2510.24776},
year = {2025}
}