Joint Dynamic Grouping and Gradient Coding for Time-Critical Distributed Machine Learning in Heterogeneous Edge Networks
Author:
Affiliation:
1. Key Laboratory of Water Big Data Technology, Ministry of Water Resources, Nanjing, China
2. College of Computer and Information, Hohai University, Nanjing, China
3. Center for Networked Computing, Temple University, Philadelphia, PA, USA
Funder
The National Natural Science Foundation of China
Postgraduate Research and Practice Innovation Program of Jiangsu Province
The Fundamental Research Funds for The Central Universities
Jiangsu Provincial Key Research and Development Program
Science Technology Achievement Transformation of Jiangsu Province
Key Technology Project of China Huaneng Group
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing
Link
http://xplorestaging.ieee.org/ielx7/6488907/9939125/09794777.pdf?arnumber=9794777
Reference40 articles.
1. FedFMC: Sequential efficient federated learning on non-IID data;kopparapu;arXiv 2006 10937,2020
2. FedCD: Improving performance in non-IID federated learning;kopparapu;arXiv 2006 09637,2020
3. Gradient Coding with Clustering and Multi-Message Communication
4. Asynchronous gateway reallocation communication in heterogeneous 5G networks
5. Communication-efficient asynchronous federated learning in resource-constrained edge computing
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