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}.

翻译：安全可靠的医学图像分类对于有效患者治疗至关重要，但集中式模型因数据和隐私问题面临挑战。联邦学习（FL）能够实现隐私保护的协作，但在异构非独立同分布数据和高通信成本方面存在困难，特别是在大型网络中。我们提出\\textbf{CFL-SparseMed}，一种采用Top-k稀疏化的联邦学习方法，通过仅传输前k个梯度来降低通信开销。这一统一解决方案在保持模型精度的同时，有效应对数据异构性问题。该方法提升了联邦学习效率，保护了隐私，并在非独立同分布医学成像场景中提高了诊断准确性和患者护理水平。可复现源代码发布于\\href{https://github.com/Aniket2241/APK_contruct}{Github}。