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Edge-Based Communication Optimization for Distributed Federated Learning
Tian Wang
, Yan Liu
, Xi Zheng
,
Hong-Ning Dai
, Weijia Jia
, Mande Xie
*
*
Corresponding author for this work
Department of Computer Science
Research output
:
Contribution to journal
›
Journal article
›
peer-review
93
Citations (Scopus)
Overview
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Dive into the research topics of 'Edge-Based Communication Optimization for Distributed Federated Learning'. Together they form a unique fingerprint.
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Keyphrases
Edge-based
100%
Federated Learning
100%
Communication Optimization
100%
End Device
100%
Latency
66%
Network Position
66%
Concurrency
66%
Local Update
66%
Model Update
33%
Convergence Rate
33%
Cloud Server
33%
Cosine Similarity
33%
Communication Cost
33%
Upload
33%
Optimization Framework
33%
Global Model
33%
Transmission Delay
33%
Local Model
33%
Local Defect
33%
Distributed Machine Learning
33%
Number of Ends
33%
Communication Round
33%
Defect Data
33%
Concurrent Access
33%
Parameter Server
33%
Mobile Edge Nodes
33%
Cloud Phone
33%
Device-to-device Communication
33%
Computer Science
Federated machine learning
100%
Network Location
66%
Concurrency
66%
Experimental Result
33%
Cosine Similarity
33%
Communication Cost
33%
Speed Convergence
33%
Optimization Framework
33%
Distributed Machine Learning
33%
Transmission Delay
33%
Parameter Server
33%
Device Communication
33%
Concurrent Access
33%