Linear Convergence in Federated Learning: Tackling Client Heterogeneity
and Sparse Gradients

Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients

14 February 2021

Rayana H. Jaafar

George J. Pappas

Papers citing "Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients"

4 / 4 papers shown

Title
Straggler-Resilient Federated Learning: Leveraging the Interplay Between Statistical Accuracy and System Heterogeneity Amirhossein Reisizadeh Isidoros Tziotis Hamed Hassani Aryan Mokhtari Ramtin Pedarsani FedML 130 80 0 28 Dec 2020
Linearly Converging Error Compensated SGD Eduard A. Gorbunov D. Kovalev Dmitry Makarenko Peter Richtárik 129 69 0 23 Oct 2020
FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization Amirhossein Reisizadeh Aryan Mokhtari Hamed Hassani Ali Jadbabaie Ramtin Pedarsani FedML 118 645 0 28 Sep 2019
Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition Hamed Karimi J. Nutini Mark W. Schmidt 102 1,090 0 16 Aug 2016