Long-Short History of Gradients is All You Need: Detecting Malicious and Unreliable Clients in Federated Learning

14 August 2022

Papers citing "Long-Short History of Gradients is All You Need: Detecting Malicious and Unreliable Clients in Federated Learning"

6 / 6 papers shown

Title
Gradient Purification: Defense Against Poisoning Attack in Decentralized Federated Learning Bin Li Xiaoye Miao Yongheng Shang Xinkui Zhao AAML 44 0 0 08 Jan 2025
Using Anomaly Detection to Detect Poisoning Attacks in Federated Learning Applications Ali Raza Shujun Li K. Tran L. Koehl Kim Duc Tran AAML 25 3 0 18 Jul 2022
Untargeted Poisoning Attack Detection in Federated Learning via Behavior Attestation Ranwa Al Mallah David López Godwin Badu-Marfo Bilal Farooq AAML 37 39 0 24 Jan 2021
FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping Xiaoyu Cao Minghong Fang Jia Liu Neil Zhenqiang Gong FedML 106 611 0 27 Dec 2020
Analyzing Federated Learning through an Adversarial Lens A. Bhagoji Supriyo Chakraborty Prateek Mittal S. Calo FedML 177 1,032 0 29 Nov 2018
New Convergence Aspects of Stochastic Gradient Algorithms Lam M. Nguyen Phuong Ha Nguyen Peter Richtárik K. Scheinberg Martin Takáč Marten van Dijk 23 65 0 10 Nov 2018