Risk-Guided Asynchronous Recovery for Federated Test-Time Adaptation under Distribution Shifts

Federated Learning (FL) enables decentralized model training without sharing raw data, yet deployed models often encounter distribution shifts. To maintain model performance under such evolving shifts, federated test-time adaptation has emerged as a promising approach that adapts deployed models using unlabeled test data and periodically leverages cross-client collaboration to recover from local adaptation errors. However, as data distributions continue to shift after deployment, existing federated test-time adaptation methods suffer from: (1) errors accumulate during long-term TTA, (2) periodic collaboration incurs redundant communication, and (3) fixed layer recovery ignores degradation severity and may overwrite useful adapted parameters. To address these issues, we propose FedART, a risk-aware asynchronous recovery framework that efficiently mitigates error accumulation under continual distribution shifts. Compared to existing methods, FedART enables each client to initiate recovery asynchronously and adaptively determines recovery scope, thereby preserving beneficial local adaptations while improving efficiency. Specifically, FedART first introduces a local adaptation risk monitoring scheme to continuously estimate state risk of each client’s local TTA, and designs a risk-triggered asynchronous collaboration mechanism to retrieve suitable client adaptation states to construct recovery candidates, avoiding error accumulation and reducing redundant communication. Then, we devise a risk-guided selective layer restoration mechanism that preserves useful adapted parameters instead of uniformly recovering layers, thereby improving model stability under continual distribution shifts. Extensive experiments across multiple benchmarks demonstrate that FedART achieves competitive or superior performance compared with strong baselines, while reducing the communication volume for TTA recovery by approximately 98% compared with periodic recovery.