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20242026
most citedAn Enhanced Federated Prototype Learning Method under Domain Shift

1 citations · 3 across the 6 of their papers we have counts for

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cs.CV2026

FedBST: Bilateral Self-Training for Unsupervised Federated Classification

Aijing Yu, Kuangpu Guo, Jian Liang +1

Unsupervised federated learning with Vision-Language Models (e.g., CLIP) offers a promising solution for label-free classification, yet its performance often degrades under severe…

cs.CV2025

Cooperative Pseudo Labeling for Unsupervised Federated Classification

Kuangpu Guo, Lijun Sheng, Yongcan Yu +3

Unsupervised Federated Learning (UFL) aims to collaboratively train a global model across distributed clients without sharing data or accessing label information. Previous UFL work…