A Huber Loss Minimization Approach to Byzantine Robust Federated
Learning

A Huber Loss Minimization Approach to Byzantine Robust Federated Learning

24 August 2023

Papers citing "A Huber Loss Minimization Approach to Byzantine Robust Federated Learning"

9 / 9 papers shown

Title
Differential Private Stochastic Optimization with Heavy-tailed Data: Towards Optimal Rates Puning Zhao Jiafei Wu Zhe Liu Chong Wang Rongfei Fan Qingming Li 40 1 0 19 Aug 2024
Byzantine-resilient Federated Learning Employing Normalized Gradients on Non-IID Datasets Shiyuan Zuo Xingrun Yan Rongfei Fan Li Shen Puning Zhao Jie Xu Han Hu FedML 25 1 0 18 Aug 2024
Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challenges Yanli Li Zhongliang Guo Nan Yang Huaming Chen Dong Yuan Weiping Ding FedML 29 2 0 09 Jul 2024
Learning with User-Level Local Differential Privacy Puning Zhao Li Shen Rongfei Fan Qingming Li Huiwen Wu Jiafei Wu Zhe Liu 21 2 0 27 May 2024
A Huber Loss Minimization Approach to Mean Estimation under User-level Differential Privacy Puning Zhao Lifeng Lai Li Shen Qingming Li Jiafei Wu Zhe Liu 44 7 0 22 May 2024
Byzantine-resilient Federated Learning With Adaptivity to Data Heterogeneity Shiyuan Zuo Xingrun Yan Rongfei Fan Han Hu Hangguan Shan Tony Q. S. Quek FedML AAML 39 6 0 20 Mar 2024
High Dimensional Distributed Gradient Descent with Arbitrary Number of Byzantine Attackers Puning Zhao Zhiguo Wan OOD FedML 36 4 0 25 Jul 2023
Byzantine-Robust Federated Learning with Optimal Statistical Rates and Privacy Guarantees Banghua Zhu Lun Wang Qi Pang Shuai Wang Jiantao Jiao D. Song Michael I. Jordan FedML 91 30 0 24 May 2022
Securing Distributed Gradient Descent in High Dimensional Statistical Learning Lili Su Jiaming Xu FedML 137 35 0 26 Apr 2018