FedChallenger: A Robust Challenge-Response and Aggregation Strategy to Defend Poisoning Attacks in Federated Learning

Growing data privacy concerns in smart applications have spurred the development of Federated Learning (FL), a novel approach enabling heterogeneous clients to jointly train a global model without exchanging private data. However, FL faces significant challenges in aggregating model updates from dif...

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Detalhes bibliográficos

Principais autores:	M. A. Moyeen, Kuljeet Kaur, Anjali Agarwal, S. Ricardo Manzano, Marzia Zaman, Nishith Goel
Formato:	Artigo
Idioma:	Inglês
Publicado em:	IEEE 2025-01-01
coleção:	IEEE Access
Assuntos:	Federated learning machine learning poisoning attacks robust aggregation zero trust security
Acesso em linha:	https://ieeexplore.ieee.org/document/11095660/
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