Federated Semi-Supervised Learning with Uniform Random and Lattice-Based Client Sampling
Federated semi-supervised learning (Fed-SSL) has emerged as a powerful framework that leverages both labeled and unlabeled data distributed across clients. To reduce communication overhead, real-world deployments often adopt partial client participation, where only a subset of clients is selected in...
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| Autori principali: | , |
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| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
MDPI AG
2025-07-01
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| Serie: | Entropy |
| Soggetti: | |
| Accesso online: | https://www.mdpi.com/1099-4300/27/8/804 |
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