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"

9 / 9 papers shown

Title
Convergence and Accuracy Trade-Offs in Federated Learning and Meta-Learning Zachary B. Charles Jakub Konecný FedML 58 63 0 08 Mar 2021
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
On the Outsized Importance of Learning Rates in Local Update Methods Zachary B. Charles Jakub Konecný FedML 43 54 0 02 Jul 2020
FedSplit: An algorithmic framework for fast federated optimization Reese Pathak Martin J. Wainwright FedML 159 183 0 11 May 2020
Adaptive Personalized Federated Learning Yuyang Deng Mohammad Mahdi Kamani M. Mahdavi FedML 281 549 0 30 Mar 2020
Gradient Descent Provably Optimizes Over-parameterized Neural Networks S. Du Xiyu Zhai Barnabás Póczós Aarti Singh MLT ODL 137 1,261 0 04 Oct 2018
Local SGD Converges Fast and Communicates Little Sebastian U. Stich FedML 152 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 269 4,620 0 18 Oct 2016
A Tutorial on Spectral Clustering U. V. Luxburg 181 10,497 0 01 Nov 2007