Auto-weighted Robust Federated Learning with Corrupted Data Sources

14 January 2021

Papers citing "Auto-weighted Robust Federated Learning with Corrupted Data Sources"

7 / 7 papers shown

Title
Robust Asymmetric Heterogeneous Federated Learning with Corrupted Clients Xiuwen Fang Mang Ye Bo Du FedML 66 1 0 12 Mar 2025
LiteChain: A Lightweight Blockchain for Verifiable and Scalable Federated Learning in Massive Edge Networks Handi Chen Rui Zhou Yun-Hin Chan Zhihan Jiang Xianhao Chen Edith C. H. Ngai 50 0 0 06 Mar 2025
Defending against the Label-flipping Attack in Federated Learning N. Jebreel J. Domingo-Ferrer David Sánchez Alberto Blanco-Justicia AAML 13 36 0 05 Jul 2022
FL-Defender: Combating Targeted Attacks in Federated Learning N. Jebreel J. Domingo-Ferrer AAML FedML 43 56 0 02 Jul 2022
FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping Xiaoyu Cao Minghong Fang Jia Liu Neil Zhenqiang Gong FedML 108 611 0 27 Dec 2020
Threats to Federated Learning: A Survey Lingjuan Lyu Han Yu Qiang Yang FedML 193 434 0 04 Mar 2020
Analyzing Federated Learning through an Adversarial Lens A. Bhagoji Supriyo Chakraborty Prateek Mittal S. Calo FedML 179 1,032 0 29 Nov 2018