Abstract
Federated learning in healthcare faces significant challenges under non-independent and identically distributed (non-IID) medical data, including communication inefficiency, unstable convergence, and privacy risks. To address these challenges, we propose MedSparseFL, a sparse and privacy-preserving federated learning framework for healthcare applications. On the client side, MedSparseFL introduces a gradient sparsification mechanism using a lightweight Gradient Score Network (GSN) to estimate the importance of gradient updates. High-contribution dimensions are preferentially selected through probabilistic sampling combined with residual feedback, thereby alleviating the adverse effects of non-IID data on global convergence. Before transmission, the sparse gradients are compressed using Count Sketch mapping and protected via sketch-domain obfuscation and pairwise masking to enhance privacy. Additive homomorphic encryption (AHE) is further applied to the compressed sketches to reduce transmission leakage risks while maintaining computational efficiency. On the server side, global gradient estimates are obtained from aggregated sketches, and the inferred sparse support is exploited to suppress anomalous updates. Experiments on two public medical datasets show that MedSparseFL improves classification performance by 2%, reduces communication overhead, and lowers empirical privacy leakage risks under the evaluated attack settings, with privacy protection positioned at the update level under the stated threat model.