arXiv Computer Vision October 23, 2026 By Aijing Yu, Kuangpu Guo, Jian Liang, Xiao-Yu Zhang

FedBST: Bilateral Self-Training for Unsupervised Federated Classification

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FedBST introduces a bilateral self‑training framework for unsupervised federated classification using Vision‑Language Models. It addresses the shortcomings of prior methods by exploiting both high‑confidence and ambiguous samples through uncertainty‑aware confidence penalization (UACP) during local training and a visual‑text ensemble strategy at inference. Experiments show that FedBST consistently outperforms existing approaches under standard federated aggregation protocols.

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