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gradient compression

78 papers

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Co-occurring keywords

communication efficiency (297) distributed learning (563) distributed training (188) stochastic gradient descent (1091) federated learning (1333) distributed optimization (275) model compression (3302) communication compression (55) error feedback (22) stochastic gradient (296)

Papers

LSHFed: Robust and Communication-Efficient Federated Learning with Locally-Sensitive Hashing Gradient Mapping AAAI 2026
SDP4Bit: Toward 4-bit Communication Quantization in Sharded Data Parallelism for LLM Training NIPS 2024
Optimal and Approximate Adaptive Stochastic Quantization NIPS 2024
THC: Accelerating Distributed Deep Learning Using Tensor Homomorphic Compression NSDI 2024
Shadowheart SGD: Distributed Asynchronous SGD with Optimal Time Complexity Under Arbitrary Computation and Communication Heterogeneity NIPS 2024
Practical Hybrid Gradient Compression for Federated Learning Systems IJCAI 2024
Language Models as Zero-shot Lossless Gradient Compressors: Towards General Neural Parameter Prior Models NIPS 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
MicroAdam: Accurate Adaptive Optimization with Low Space Overhead and Provable Convergence NIPS 2024
Don't Compress Gradients in Random Reshuffling: Compress Gradient Differences NIPS 2024
EF21-P and Friends: Improved Theoretical Communication Complexity for Distributed Optimization with Bidirectional Compression ICML 2023
Analysis of Error Feedback in Federated Non-Convex Optimization with Biased Compression: Fast Convergence and Partial Participation ICML 2023
SLAMB: Accelerated Large Batch Training with Sparse Communication ICML 2023
Birder: Communication-Efficient 1-bit Adaptive Optimizer for Practical Distributed DNN Training NIPS 2023
SWARM Parallelism: Training Large Models Can Be Surprisingly Communication-Efficient ICML 2023
Unbiased Compression Saves Communication in Distributed Optimization: When and How Much? NIPS 2023
Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design ICML 2023
Communication-efficient Federated Learning with Single-Step Synthetic Features Compressor for Faster Convergence ICCV 2023
Similarity, Compression and Local Steps: Three Pillars of Efficient Communications for Distributed Variational Inequalities NIPS 2023
QLSD: Quantised Langevin Stochastic Dynamics for Bayesian Federated Learning AISTATS 2022
ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training ICML 2022
Communication-Efficient Adaptive Federated Learning ICML 2022
Communication-Compressed Adaptive Gradient Method for Distributed Nonconvex Optimization AISTATS 2022
Detached Error Feedback for Distributed SGD with Random Sparsification ICML 2022