Weight Divergence Driven Divide-and-Conquer Approach for Optimal
Federated Learning from non-IID Data

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Weight Divergence Driven Divide-and-Conquer Approach for Optimal Federated Learning from non-IID Data

28 June 2021

Pravin Chandran

Raghavendra Bhat

Avinash Chakravarthi

Srikanth Chandar

ArXiv (abs)PDF HTML

Papers citing "Weight Divergence Driven Divide-and-Conquer Approach for Optimal Federated Learning from non-IID Data"

6 / 6 papers shown

Title
An Efficient Framework for Clustered Federated Learning Avishek Ghosh Jichan Chung Dong Yin Kannan Ramchandran FedML 79 865 0 07 Jun 2020
Federated Learning of a Mixture of Global and Local Models Filip Hanzely Peter Richtárik FedML 59 385 0 10 Feb 2020
Overcoming Forgetting in Federated Learning on Non-IID Data N. Shoham Tomer Avidor Aviv Keren Nadav Tal-Israel Daniel Benditkis Liron Mor Yosef Itai Zeitak CLL FedML 130 226 0 17 Oct 2019
Towards Federated Learning at Scale: System Design Keith Bonawitz Hubert Eichner W. Grieskamp Dzmitry Huba A. Ingerman ... H. B. McMahan Timon Van Overveldt David Petrou Daniel Ramage Jason Roselander FedML 124 2,674 0 04 Feb 2019
LEAF: A Benchmark for Federated Settings S. Caldas Sai Meher Karthik Duddu Peter Wu Tian Li Jakub Konecný H. B. McMahan Virginia Smith Ameet Talwalkar FedML 153 1,422 0 03 Dec 2018
Visualizing and Understanding Convolutional Networks Matthew D. Zeiler Rob Fergus FAtt SSL 595 15,902 0 12 Nov 2013

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