Buchumschlag

Fast, Private, and Protected: Safeguarding Data Privacy and Defending Against Model Poisoning Attacks in Federated Learning

Fuente: arXiv

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Bibliographische Detailangaben
Hauptverfasser:	Assumpcao, Nicolas Riccieri Gardin, Villas, Leandro
Format:	Preprint
Veröffentlicht:	2025
Schlagworte:	Machine Learning
Online-Zugang:	Acceder al recurso
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

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