Enhanced federated learning framework for handling heterogeneity in medical image data
摘要
The application of artificial intelligence (AI) in medical imaging has resulted in major advancements in patient outcomes, individualized treatment strategies, and diagnostic accuracy. However, the sensitive nature of medical data and strict privacy regulations pose challenges for data sharing across institutions. Federated Learning (FL) offers a solution by enabling collaborative model training without exchanging raw data, thereby preserving privacy. Despite these benefits, FL faces substantial challenges when dealing with heterogeneous data distributions commonly found in real-world medical scenarios, which can degrade model performance. To address this issue, this work introduces a novel FL framework that integrates Knowledge Distillation (KD) to address the challenges of data heterogeneity in medical imaging. Unlike traditional FL methods, our approach performs knowledge distillation at the feature level, prior to classification, enabling more effective knowledge transfer between the server and clients. Extensive experiments on three benchmark datasets–TissueMNIST, PathMNIST, and BloodMNIST–under two non-IID scenarios (Label Skew and Dirichlet distribution with