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...

詳細記述

書誌詳細

主要な著者:	M. A. Moyeen, Kuljeet Kaur, Anjali Agarwal, S. Ricardo Manzano, Marzia Zaman, Nishith Goel
フォーマット:	Artigo
言語:	Inglês
出版事項:	IEEE 2025-01-01
シリーズ:	IEEE Access
主題:	Federated learning machine learning poisoning attacks robust aggregation zero trust security
オンライン･アクセス:	https://ieeexplore.ieee.org/document/11095660/
タグ:	タグ追加 タグなし, このレコードへの初めてのタグを付けませんか!