client drift

CycleSL: Server-Client Cyclical Update Driven Scalable Split Learning WACV 2026 DP-FedAdamW: An Efficient Optimizer for Differentially Private Federated Large Models CVPR 2026 FedBRICK: Structural Bias Aware Heterogeneous Foundation Model Federated Tuning AAAI 2026 FedMerge: Federated Model Merging for Personalization AAAI 2026 PPFL: A Parameter Behavior-Driven Plug-in Personalization Engine for Federated Learning AAAI 2026 Creating Coherence in Federated Non-Negative Matrix Factorization AAAI 2025 Federated Learning with Sample-level Client Drift Mitigation AAAI 2025 Federated Residual Low-Rank Adaptation of Large Language Models ICLR 2025 First-Order Federated Bilevel Learning AAAI 2025 Large Language Models Enhanced Personalized Graph Neural Architecture Search in Federated Learning AAAI 2025 Accelerating Heterogeneous Federated Learning with Closed-form Classifiers ICML 2024 An Aggregation-Free Federated Learning for Tackling Data Heterogeneity CVPR 2024 Cooperative Hardware-Prompt Learning for Snapshot Compressive Imaging NeurIPS 2024 FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices Using a Computing Power-Aware Scheduler ICLR 2024 Federated Optimization with Doubly Regularized Drift Correction ICML 2024 Federated Partial Label Learning with Local-Adaptive Augmentation and Regularization AAAI 2024 FedImpro: Measuring and Improving Client Update in Federated Learning ICLR 2024 Momentum Benefits Non-iid Federated Learning Simply and Provably ICLR 2024 Nonconvex Federated Learning on Compact Smooth Submanifolds With Heterogeneous Data NeurIPS 2024 Personalized Federated Learning for Cross-City Traffic Prediction IJCAI 2024 SCAFFLSA: Taming Heterogeneity in Federated Linear Stochastic Approximation and TD Learning NeurIPS 2024 Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth Landscape ICML 2023 Elastic Aggregation for Federated Optimization CVPR 2023 Federated Averaging Langevin Dynamics: Toward a unified theory and new algorithms AISTATS 2023 Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization EMNLP 2023