Dynamic Byzantine-Robust Learning: Adapting to Switching Byzantine Workers

Byzantine-robust learning has emerged as a prominent fault-tolerant distributed machine learning framework. However, most techniques focus on the static setting, wherein the identity of Byzantine workers remains unchanged throughout the learning process. This assumption fails to capture real-world d...

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Hauptverfasser:	Dorfman, Ron, Yehya, Naseem, Levy, Kfir Y
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Distributed, Parallel, and Cluster Computing Computer Science - Learning Statistics - Machine Learning
Online-Zugang:	Volltext bestellen
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