Uncertainty Principle for Communication Compression in Distributed and Federated Learning and the Search for an Optimal Compressor

20 February 2020

Papers citing "Uncertainty Principle for Communication Compression in Distributed and Federated Learning and the Search for an Optimal Compressor"

10 / 10 papers shown

Title
Correlated Quantization for Faster Nonconvex Distributed Optimization Andrei Panferov Yury Demidovich Ahmad Rammal Peter Richtárik MQ 25 4 0 10 Jan 2024
Matrix Compression via Randomized Low Rank and Low Precision Factorization R. Saha Varun Srivastava Mert Pilanci 13 19 0 17 Oct 2023
Lower Bounds and Accelerated Algorithms in Distributed Stochastic Optimization with Communication Compression Yutong He Xinmeng Huang Yiming Chen W. Yin Kun Yuan 26 7 0 12 May 2023
Breaking the Communication-Privacy-Accuracy Tradeoff with $f$ -Differential Privacy Richeng Jin Z. Su C. Zhong Zhaoyang Zhang Tony Q. S. Quek H. Dai FedML 19 2 0 19 Feb 2023
CEDAS: A Compressed Decentralized Stochastic Gradient Method with Improved Convergence Kun-Yen Huang Shin-Yi Pu 30 9 0 14 Jan 2023
Minimax Optimal Quantization of Linear Models: Information-Theoretic Limits and Efficient Algorithms R. Saha Mert Pilanci Andrea J. Goldsmith MQ 17 3 0 23 Feb 2022
Wyner-Ziv Gradient Compression for Federated Learning Kai Liang Huiru Zhong Haoning Chen Youlong Wu FedML 14 8 0 16 Nov 2021
Rethinking gradient sparsification as total error minimization Atal Narayan Sahu Aritra Dutta A. Abdelmoniem Trambak Banerjee Marco Canini Panos Kalnis 37 54 0 02 Aug 2021
Efficient Randomized Subspace Embeddings for Distributed Optimization under a Communication Budget R. Saha Mert Pilanci Andrea J. Goldsmith 11 5 0 13 Mar 2021
MARINA: Faster Non-Convex Distributed Learning with Compression Eduard A. Gorbunov Konstantin Burlachenko Zhize Li Peter Richtárik 28 108 0 15 Feb 2021