client sampling

Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback JMLR 2025 Client2Vec: Improving Federated Learning by Distribution Shifts Aware Client Indexing ICCV 2025 Byzantine Robustness and Partial Participation Can Be Achieved at Once: Just Clip Gradient Differences NeurIPS 2024 FLHetBench: Benchmarking Device and State Heterogeneity in Federated Learning CVPR 2024 Heterogeneity-Guided Client Sampling: Towards Fast and Efficient Non-IID Federated Learning NeurIPS 2024 Achieving Linear Speedup in Non-IID Federated Bilevel Learning ICML 2023 Beyond ADMM: A Unified Client-Variance-Reduced Adaptive Federated Learning Framework AAAI 2023 Can 5th Generation Local Training Methods Support Client Sampling? Yes! AISTATS 2023 DELTA: Diverse Client Sampling for Fasting Federated Learning NeurIPS 2023 Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction ICML 2023 FedSampling: A Better Sampling Strategy for Federated Learning IJCAI 2023 On the Convergence of Federated Averaging with Cyclic Client Participation ICML 2023 SimFBO: Towards Simple, Flexible and Communication-efficient Federated Bilevel Learning NeurIPS 2023 QSFL: A Two-Level Uplink Communication Optimization Framework for Federated Learning ICML 2022 SAGDA: Achieving O(ε^-2) Communication Complexity in Federated Min-Max Learning NeurIPS 2022 Federated Multi-Armed Bandits AAAI 2021 On Large-Cohort Training for Federated Learning NeurIPS 2021