A Non-parametric View of FedAvg and FedProx: Beyond Stationary Points

A Non-parametric View of FedAvg and FedProx: Beyond Stationary Points

29 June 2021

Lili Su

Jiaming Xu

Pengkun Yang

Papers citing "A Non-parametric View of FedAvg and FedProx: Beyond Stationary Points"

7 / 7 papers shown

Title
Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients A. Mitra Rayana H. Jaafar George J. Pappas Hamed Hassani FedML 72 158 0 14 Feb 2021
FedSplit: An algorithmic framework for fast federated optimization Reese Pathak Martin J. Wainwright FedML 149 183 0 11 May 2020
Adaptive Personalized Federated Learning Yuyang Deng Mohammad Mahdi Kamani M. Mahdavi FedML 278 549 0 30 Mar 2020
Gradient Descent Provably Optimizes Over-parameterized Neural Networks S. Du Xiyu Zhai Barnabás Póczós Aarti Singh MLT ODL 130 1,261 0 04 Oct 2018
Local SGD Converges Fast and Communicates Little Sebastian U. Stich FedML 150 1,056 0 24 May 2018
Federated Learning: Strategies for Improving Communication Efficiency Jakub Konecný H. B. McMahan Felix X. Yu Peter Richtárik A. Suresh Dave Bacon FedML 267 4,620 0 18 Oct 2016
A Tutorial on Spectral Clustering U. V. Luxburg 175 10,497 0 01 Nov 2007