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

Fuente: arXiv

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Bibliographic Details
Main Authors:	Assumpcao, Nicolas Riccieri Gardin, Villas, Leandro
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
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Internet

https://arxiv.org/abs/2511.02797