MSAFed: Generalized Multi-Stage and Adaptive Federated Learning for Test-Time Medical Segmentation.

Abstract

Federated learning (FL) enables collaborative model training across multiple medical centers without sharing data, offering significant promise for privacy-preserving AI in healthcare. However, FL models often lack generalization across all participating clients (inside FL) and perform poorly when deployed to unseen clients (outside FL), particularly in heterogeneous domains. Current test-time adaptation methods for outside FL fail to address biases in personalized models toward source distributions, limiting their clinical applications. To tackle these challenges, we propose MSAFed, a generalized multi-stage adaptive FL framework that enhances both inside generalization and outside test-time adaptation. During pretraining, intra-client and inter-client contrastive learning with prototype-aware aggregation produces a generalized global model. An adaptive learning rate strategy further improves inside FL generalization. For unseen clients, source knowledge, including adaptive learning rates and prototypes, is leveraged to dynamically adapt the network architecture during test time. Experiments on three real-world multi-center medical datasets demonstrate the effectiveness of MSAFed, achieving superior performance on both inside and outside FL tasks.