Review

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

Chen Zhang Ge Su Huaxia Zhou Lufeng Hao Feng-Lin Zhu Jintai Chen Yuxiang Cai Jianwei Yin Hongxiao Xu

Jul 2026 · International Journal of Computer Vision · Vol 134 · 0 citations · 163 references

Computer Science

TL;DR

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.

DOI Semantic Scholar

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