Decentralized Machine Learning Training: A Survey on Synchronization, Consolidation, and Topologies
Author:
Affiliation:
1. Department of Computer Engineering, Jeju National University, Jeju-si, Republic of Korea
2. Department of Computer Science, COMSATS University Islamabad, Attock Campus, Attock, Pakistan
Funder
Research Grant of Jeju National University, in 2022
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/10005208/10147820.pdf?arnumber=10147820
Reference105 articles.
1. Expanding the reach of federated learning by reducing client resource requirements;caldas;arXiv 1812 07210,2018
2. Semi-decentralized federated learning with cooperative D2D local model aggregations;po-chen lin;arXiv 2103 10481,2021
3. Evaluating the Communication Efficiency in Federated Learning Algorithms
4. Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge
5. Swarm learning for decentralized healthcare
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