byzantine robustness

Byzantine-Robust and Differentially Private Federated Optimization under Weaker Assumptions UAI 2026 On the Relevance of Byzantine Robust Optimization Against Data Poisoning JMLR 2026 EBS-CFL: Efficient and Byzantine-robust Secure Clustered Federated Learning AAAI 2025 Rethinking Byzantine Robustness in Federated Recommendation from Sparse Aggregation Perspective AAAI 2025 A Huber Loss Minimization Approach to Byzantine Robust Federated Learning AAAI 2024 BOBA: Byzantine-Robust Federated Learning with Label Skewness AISTATS 2024 Byzantine-robust Decentralized Federated Learning via Dual-domain Clustering and Trust Bootstrapping CVPR 2024 Byzantine-Robust Decentralized Learning via Remove-then-Clip Aggregation AAAI 2024 Byzantine Robustness and Partial Participation Can Be Achieved at Once: Just Clip Gradient Differences NeurIPS 2024 Communication Compression for Byzantine Robust Learning: New Efficient Algorithms and Improved Rates AISTATS 2024 Simple Minimax Optimal Byzantine Robust Algorithm for Nonconvex Objectives with Uniform Gradient Heterogeneity ICLR 2024 Tolerating Outliers: Gradient-Based Penalties for Byzantine Robustness and Inclusion IJCAI 2024 Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML NeurIPS 2024 Byzantine-Robust Federated Learning with Optimal Statistical Rates AISTATS 2023 Byzantine-Tolerant Methods for Distributed Variational Inequalities NeurIPS 2023 DeFL: Defending against Model Poisoning Attacks in Federated Learning via Critical Learning Periods Awareness AAAI 2023 Variance Reduction is an Antidote to Byzantines: Better Rates, Weaker Assumptions and Communication Compression as a Cherry on the Top ICLR 2023 ApproxIFER: A Model-Agnostic Approach to Resilient and Robust Prediction Serving Systems AAAI 2022 Bridging Differential Privacy and Byzantine-Robustness via Model Aggregation IJCAI 2022 Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing ICLR 2022 Renyi Differential Privacy of Propose-Test-Release and Applications to Private and Robust Machine Learning NeurIPS 2022 Learning from History for Byzantine Robust Optimization ICML 2021 Provably Secure Federated Learning against Malicious Clients AAAI 2021 Distributed Training with Heterogeneous Data: Bridging Median- and Mean-Based Algorithms NeurIPS 2020 Election Coding for Distributed Learning: Protecting SignSGD against Byzantine Attacks NeurIPS 2020