Lufeng Hao

Federated Learning Meets Test-Time Adaptation: Methods, Challenges, and Future Directions

A comprehensive survey of FedTTA is provided, formalizing its problem setting and establishing a unified taxonomy encompassing three paradigms: i) Federated Initialization and Test Fine-tuning, where the global model serves as a robust prior for local refinement; ii) Federated Shared Backbone and Test Personalized Adaptation, where feature extraction is decoupled from lightweight local adapters.