PPFedKD: Privacy‐Preserving Federated Learning and Adaptive Contrastive Distillation for Medical Image Diagnosis

Abstract

Federated learning (FL) in medical image analysis offers privacy‐preserving collaborative training but faces challenges such as the impact of Non‐IID data on model performance and communication overhead that limits system efficiency. Existing methods often struggle to balance data heterogeneity with privacy protection in high‐dimensional pathological image classification tasks. To overcome these challenges, we propose a Privacy‐Preserving Self‐Distillation Federated Framework (PPFedKD) that enhances both model performance and privacy security through optimized feature learning and communication aggregation. The proposed framework introduces a adaptive contrastive distillation strategy, leveraging spatial and semantic augmentations to produce consistent feature representations and mitigate imbalances in client data distributions. It also employs a semi‐asynchronous dynamic aggregation mechanism with a lag threshold, allowing the server to accept slightly outdated updates, thereby reducing communication redundancy and wait times. Furthermore, privacy‐enhanced gradient perturbation is applied on the client side using a noise constraint strategy to effectively mitigate privacy leakage while ensuring stable convergence. Experimental results on diabetic retinopathy and breast cancer pathology datasets demonstrate that PPFedKD outperforms baseline methods in classification accuracy, privacy protection, and communication efficiency, providing a secure and effective solution for medical image classification.