FedEvi: Improving Federated Medical Image Segmentation via Evidential Weight Aggregation

Federated learning enables collaborative knowledge acquisition among clinical institutions while preserving data privacy. However, feature heterogeneity across institutions can compromise the global model’s performance and generalization capability. Existing methods often adjust aggregation weights dynamically to improve the global model’s generalization but rely heavily on the local models’ performance or reliability, excluding an explicit measure of the generalization gap arising from deploying the global model across varied local datasets. To address this issue, we propose FedEvi, a method that adjusts the aggregation weights based on the generalization gap between the global model and each local dataset and the reliability of local models. We utilize a Dirichlet-based evidential model to disentangle the uncertainty representation of each local model and the global model into epistemic uncertainty and aleatoric uncertainty. Then, we quantify the global generalization gap using the epistemic uncertainty of the global model and assess the reliability of each local model using its aleatoric uncertainty. Afterward, we design aggregation weights using the global generalization gap and local reliability. Comprehensive experimentation reveals that FedEvi consistently surpasses 12 state-of-the-art methods across three real-world multi-center medical image segmentation tasks, demonstrating the effectiveness of FedEvi in bolstering the generalization capacity of the global model in heterogeneous federated scenarios. The code will be available at https://github.com/JiayiChen815/FedEvi.