gradient compression

LSHFed: Robust and Communication-Efficient Federated Learning with Locally-Sensitive Hashing Gradient Mapping AAAI 2026 Markovian Compression: Looking to the Past Helps Accelerate the Future UAI 2026 Beyond Communication Overhead: A Multilevel Monte Carlo Approach for Mitigating Compression Bias in Distributed Learning ICML 2025 On the Optimization Landscape of Low Rank Adaptation Methods for Large Language Models ICLR 2025 SEPARATE: A Simple Low-rank Projection for Gradient Compression in Modern Large-scale Model Training Process ICLR 2025 Unbiased and Sign Compression in Distributed Learning: Comparing Noise Resilience via SDEs AISTATS 2025 Don't Compress Gradients in Random Reshuffling: Compress Gradient Differences NeurIPS 2024 Flora: Low-Rank Adapters Are Secretly Gradient Compressors ICML 2024 Language Models as Zero-shot Lossless Gradient Compressors: Towards General Neural Parameter Prior Models NeurIPS 2024 MicroAdam: Accurate Adaptive Optimization with Low Space Overhead and Provable Convergence NeurIPS 2024 Optimal and Approximate Adaptive Stochastic Quantization NeurIPS 2024 Practical Hybrid Gradient Compression for Federated Learning Systems IJCAI 2024 SDP4Bit: Toward 4-bit Communication Quantization in Sharded Data Parallelism for LLM Training 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 THC: Accelerating Distributed Deep Learning Using Tensor Homomorphic Compression NSDI 2024 Token-wise Influential Training Data Retrieval for Large Language Models ACL 2024 z-SignFedAvg: A Unified Stochastic Sign-Based Compression for Federated Learning AAAI 2024 Analysis of Error Feedback in Federated Non-Convex Optimization with Biased Compression: Fast Convergence and Partial Participation ICML 2023 Birder: Communication-Efficient 1-bit Adaptive Optimizer for Practical Distributed DNN Training NeurIPS 2023 Communication-efficient Federated Learning with Single-Step Synthetic Features Compressor for Faster Convergence ICCV 2023 EF21-P and Friends: Improved Theoretical Communication Complexity for Distributed Optimization with Bidirectional Compression ICML 2023 Maximizing Communication Efficiency for Large-scale Training via 0/1 Adam ICLR 2023 Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design ICML 2023 Similarity, Compression and Local Steps: Three Pillars of Efficient Communications for Distributed Variational Inequalities NeurIPS 2023