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

• URL: http://arxiv.org/abs/2510.24776v1
• Date: Sat, 25 Oct 2025 09:13:06 GMT
• Title: CFL-SparseMed: Communication-Efficient Federated Learning for Medical Imaging with Top-k Sparse Updates
• Abstract summary: Federated Learning (FL) enables privacy-preserving collaborations but struggles with heterogeneous, non-IID data and high communication costs.<n>We propose textbfCFL-SparseMed, an FL approach that uses Top-k Sparsification to reduce communication overhead by transmitting only the top k gradients.
• Score: 0.11630611994349026
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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}.

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