# CFL-SparseMed: Communication-Efficient Federated Learning for Medical Imaging with Top-k Sparse Updates

Source: [CFL-SparseMed: Communication-Efficient Federated Learning for Medical Imaging with Top-k Sparse Updates](<https://www.sota2.com/research/paper/6902f0f13ef7ccd74e5e4247>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-31T05:05:16.275Z

Authors: Gousia Habib, Aniket Bhardwaj, Ritvik Sharma, Shoeib Amin Banday, Ishfaq Ahmad Malik

Published: 2025-10-25T09:13:06.000Z

## 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}.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2510.24776>)

[PDF](<https://arxiv.org/pdf/2510.24776.pdf>)