3 papers
cs.LG2026
Enhanced Byzantine-Robust Federated Learning Via Truncated-Quadratic Loss for Heterogeneous Data
Zhi-Yong Wang, Hao Nan Sheng, Werner Stefan +3
Federated learning distributes data among clients, making it vulnerable to malicious attacks and data heterogeneity, which together pose challenges for robust learning. To tack…
cs.LG2025
Communication-Efficient and Privacy-Adaptable Mechanism for Federated Learning
Chih Wei Ling, Chun Hei Michael Shiu, Youqi Wu +4
Training machine learning models on decentralized private data via federated learning (FL) poses two key challenges: communication efficiency and privacy protection. In this work,…
cs.LG2022
Adaptive Top-K in SGD for Communication-Efficient Distributed Learning
Mengzhe Ruan, Guangfeng Yan, Yuanzhang Xiao +2
Distributed stochastic gradient descent (SGD) with gradient compression has become a popular communication-efficient solution for accelerating distributed learning. One commonly us…