FedLGA: Toward System-Heterogeneity of Federated Learning via Local Gradient Approximation-Reference-Cited by-同舟云学术

FedLGA: Toward System-Heterogeneity of Federated Learning via Local Gradient Approximation

Published:2024-01 Issue:1 Volume:54 Page:401-414
ISSN:2168-2267
Container-title:IEEE Transactions on Cybernetics
language:
Short-container-title:IEEE Trans. Cybern.

Author:

Li Xingyu¹^ORCID,Qu Zhe²^ORCID,Tang Bo³^ORCID,Lu Zhuo²^ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS, USA

2. Department of Electrical Engineering, University of South Florida, Tampa, FL, USA

3. Department of Electrical and Computer Engineering, Worcester Polytechnic Institute, Worcester, MA, USA

Funder

U.S. National Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Human-Computer Interaction,Information Systems,Control and Systems Engineering,Software

Link

https://ieeexplore.ieee.org/ielam/6221036/10367425/10063977-aam.pdf

Reference81 articles.

1. Federated learning: Strategies for improving communication efficiency;Konečnỳ;arXiv:1610.05492,2016

2. Communication-efficient learning of deep networks from decentralized data;McMahan

3. An Augmented Game Approach for Design and Analysis of Distributed Learning Dynamics in Multiagent Games

4. Distributed Random Projection Algorithm for Convex Optimization

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