client heterogeneity

Understanding Generalization of Federated Learning via Stability: Heterogeneity Matters AISTATS 2024 Client-Customized Adaptation for Parameter-Efficient Federated Learning ACL 2023 Confidence-Aware Personalized Federated Learning via Variational Expectation Maximization CVPR 2023 DynaFed: Tackling Client Data Heterogeneity With Global Dynamics CVPR 2023 Efficient Personalized Federated Learning via Sparse Model-Adaptation ICML 2023 FedCR: Personalized Federated Learning Based on Across-Client Common Representation with Conditional Mutual Information Regularization ICML 2023 Federated Learning for Semantic Parsing: Task Formulation, Evaluation Setup, New Algorithms ACL 2023 How To Prevent the Poor Performance Clients for Personalized Federated Learning? CVPR 2023 Knowledge-Aware Federated Active Learning with Non-IID Data ICCV 2023 PRIOR: Personalized Prior for Reactivating the Information Overlooked in Federated Learning. NeurIPS 2023 ActPerFL: Active Personalized Federated Learning ACL 2022 Asynchronous Personalized Federated Learning with Irregular Clients ACML 2022 Faster non-convex federated learning via global and local momentum UAI 2022 FedChain: Chained Algorithms for Near-optimal Communication Cost in Federated Learning ICLR 2022 Federated Functional Gradient Boosting AISTATS 2022 FedProto: Federated Prototype Learning across Heterogeneous Clients AAAI 2022 Layer-Wised Model Aggregation for Personalized Federated Learning CVPR 2022 Personalized Online Federated Learning with Multiple Kernels NeurIPS 2022 pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learning NeurIPS 2022 Resource-Adaptive Federated Learning with All-In-One Neural Composition NeurIPS 2022 Adaptive Federated Optimization ICLR 2021 Federated Reconstruction: Partially Local Federated Learning NeurIPS 2021 Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients NeurIPS 2021 MARINA: Faster Non-Convex Distributed Learning with Compression ICML 2021 PFedAtt: Attention-based Personalized Federated Learning on Heterogeneous Clients ACML 2021