communication compression

1-bit Adam: Communication Efficient Large-Scale Training with Adam’s Convergence Speed ICML 2021 A Better Alternative to Error Feedback for Communication-Efficient Distributed Learning ICLR 2021 A Linearly Convergent Algorithm for Decentralized Optimization: Sending Less Bits for Free! AISTATS 2021 CANITA: Faster Rates for Distributed Convex Optimization with Communication Compression NeurIPS 2021 DeepReduce: A Sparse-tensor Communication Framework for Federated Deep Learning NeurIPS 2021 Distributed Second Order Methods with Fast Rates and Compressed Communication ICML 2021 DRIVE: One-bit Distributed Mean Estimation NeurIPS 2021 EF21: A New, Simpler, Theoretically Better, and Practically Faster Error Feedback NeurIPS 2021 Linear Convergent Decentralized Optimization with Compression ICLR 2021 MARINA: Faster Non-Convex Distributed Learning with Compression ICML 2021 Preserved central model for faster bidirectional compression in distributed settings NeurIPS 2021 Smoothness Matrices Beat Smoothness Constants: Better Communication Compression Techniques for Distributed Optimization NeurIPS 2021 Decentralized Deep Learning with Arbitrary Communication Compression ICLR 2020 Distributed Training with Heterogeneous Data: Bridging Median- and Mean-Based Algorithms NeurIPS 2020 On the Discrepancy between the Theoretical Analysis and Practical Implementations of Compressed Communication for Distributed Deep Learning AAAI 2020 Quantized Compressive Sampling of Stochastic Gradients for Efficient Communication in Distributed Deep Learning AAAI 2020 A Linear Speedup Analysis of Distributed Deep Learning with Sparse and Quantized Communication NeurIPS 2018 Communication Compression for Decentralized Training NeurIPS 2018 Sparsified SGD with Memory NeurIPS 2018