Federated Learning with Layer-Wise Contribution Estimation and Consensus-Based Parameter Personalization for Medical Image Classification

Personalized Federated Learning (PFL) presents a promising paradigm for mitigating privacy concerns and addressing data heterogeneity in medical images. However, the presence of data heterogeneity can introduce bias into the global model. As a result, the biased global model may degrade local performance in the personalization phase. To tackle this challenge, we propose FedLP, a novel framework that enhances global aggregation fairness and improves personalized performance. First, we propose a layer-wise aggregation strategy on the server, where it dynamically estimates each client's contribution based on the gradient discrepancies. Consider the aggregated global model may not consistently align with local distributions, we then introduce an adaptive parameter personalization process on the client side. By selectively instituting local parameters with the global model parameter with maximizing consensus optimization, clients can better retain global knowledge they need and maintain important model parameters to local data distributions. Experimental results demonstrate that FedLP outperforms state-of-the-art methods and achieves fairer performance for several medical image classification tasks.