communication compression

OS-Fed: One Snapshot Is All You Need CVPR 2026 Personalized Federated Learning with Bidirectional Communication Compression via One-Bit Random Sketching AAAI 2026 RCMoE: A Communication-Efficient Random Compression Framework for Resource-Constrained Mixture-of-Experts Training AAAI 2026 Accelerated Methods with Compressed Communications for Distributed Optimization Problems Under Data Similarity AAAI 2025 EF21 with Bells & Whistles: Six Algorithmic Extensions of Modern Error Feedback JMLR 2025 EFSkip: A New Error Feedback with Linear Speedup for Compressed Federated Learning with Arbitrary Data Heterogeneity AAAI 2025 SADDLe: Sharpness-Aware Decentralized Deep Learning with Heterogeneous Data WACV 2025 Towards Faster Decentralized Stochastic Optimization with Communication Compression ICLR 2025 Adaptive Compression in Federated Learning via Side Information AISTATS 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 Communication-Efficient Federated Learning With Data and Client Heterogeneity AISTATS 2024 Compression with Exact Error Distribution for Federated Learning AISTATS 2024 Distributed Bilevel Optimization with Communication Compression ICML 2024 EControl: Fast Distributed Optimization with Compression and Error Control ICLR 2024 Error Feedback Reloaded: From Quadratic to Arithmetic Mean of Smoothness Constants ICLR 2024 Escaping Saddle Points in Heterogeneous Federated Learning via Distributed SGD with Communication Compression AISTATS 2024 Finite-Time Convergence Rates of Decentralized Local Markovian Stochastic Approximation IJCAI 2024 Improving the Worst-Case Bidirectional Communication Complexity for Nonconvex Distributed Optimization under Function Similarity NeurIPS 2024 Shadowheart SGD: Distributed Asynchronous SGD with Optimal Time Complexity Under Arbitrary Computation and Communication Heterogeneity NeurIPS 2024 Stochastic Controlled Averaging for Federated Learning with Communication Compression ICLR 2024 Towards a Better Theoretical Understanding of Independent Subnetwork Training ICML 2024 2Direction: Theoretically Faster Distributed Training with Bidirectional Communication Compression NeurIPS 2023 A Computation and Communication Efficient Method for Distributed Nonconvex Problems in the Partial Participation Setting NeurIPS 2023 Breaking the Communication-Privacy-Accuracy Tradeoff with f-Differential Privacy NeurIPS 2023