Improving Accelerated Federated Learning with Compression and Importance Sampling

5 June 2023

Papers citing "Improving Accelerated Federated Learning with Compression and Importance Sampling"

8 / 8 papers shown

Title
A-FedPD: Aligning Dual-Drift is All Federated Primal-Dual Learning Needs Yan Sun Li Shen Dacheng Tao FedML 18 0 0 27 Sep 2024
Correlated Quantization for Faster Nonconvex Distributed Optimization Andrei Panferov Yury Demidovich Ahmad Rammal Peter Richtárik MQ 23 4 0 10 Jan 2024
DualFL: A Duality-based Federated Learning Algorithm with Communication Acceleration in the General Convex Regime Jongho Park Jinchao Xu FedML 42 1 0 17 May 2023
GradSkip: Communication-Accelerated Local Gradient Methods with Better Computational Complexity A. Maranjyan M. Safaryan Peter Richtárik 26 13 0 28 Oct 2022
Permutation Compressors for Provably Faster Distributed Nonconvex Optimization Rafal Szlendak A. Tyurin Peter Richtárik 113 35 0 07 Oct 2021
A Field Guide to Federated Optimization Jianyu Wang Zachary B. Charles Zheng Xu Gauri Joshi H. B. McMahan ... Mi Zhang Tong Zhang Chunxiang Zheng Chen Zhu Wennan Zhu FedML 173 410 0 14 Jul 2021
Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients A. Mitra Rayana H. Jaafar George J. Pappas Hamed Hassani FedML 55 157 0 14 Feb 2021
FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization Amirhossein Reisizadeh Aryan Mokhtari Hamed Hassani Ali Jadbabaie Ramtin Pedarsani FedML 157 758 0 28 Sep 2019