FedBST: Bilateral Self-Training for Unsupervised Federated Classification
arXiv:2610.30634v1 Announce Type: new Abstract: 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 non-IID shifts. A pioneering method, FedCoPL, typically relies on self-…
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- 2026-10-23 04:00 · arXiv cs.CV
FedBST: Bilateral Self-Training for Unsupervised Federated Classification