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pro vyhledávání: '"Arel, Ron"'
Autor:
Tamirisa, Rishub, Bharathi, Bhrugu, Phan, Long, Zhou, Andy, Gatti, Alice, Suresh, Tarun, Lin, Maxwell, Wang, Justin, Wang, Rowan, Arel, Ron, Zou, Andy, Song, Dawn, Li, Bo, Hendrycks, Dan, Mazeika, Mantas
Rapid advances in the capabilities of large language models (LLMs) have raised widespread concerns regarding their potential for malicious use. Open-weight LLMs present unique challenges, as existing safeguards lack robustness to tampering attacks th
Externí odkaz:
http://arxiv.org/abs/2408.00761
Standard federated learning approaches suffer when client data distributions have sufficient heterogeneity. Recent methods addressed the client data heterogeneity issue via personalized federated learning (PFL) - a class of FL algorithms aiming to pe
Externí odkaz:
http://arxiv.org/abs/2404.02478
FedSelect: Customized Selection of Parameters for Fine-Tuning during Personalized Federated Learning
Publikováno v:
International Workshop on Federated Learning and Analytics in Practice: Algorithms, Systems, Applications, and Opportunities in Conjunction with ICML 2023
Recent advancements in federated learning (FL) seek to increase client-level performance by fine-tuning client parameters on local data or personalizing architectures for the local task. Existing methods for such personalization either prune a global
Externí odkaz:
http://arxiv.org/abs/2306.13264