Adaptive Client Selection in Resource Constrained Federated Learning Systems: A Deep Reinforcement Learning Approach

journal contribution

posted on 2024-06-13, 12:07 authored by H Zhang, Z Xie, R Zarei, T Wu, K Chen

Adaptive Client Selection in Resource Constrained Federated Learning Systems: A Deep Reinforcement Learning Approach

History

Journal

IEEE Access

Volume

9

Pagination

98423-98432

Location

Piscataway, N.J.

Open access

Yes

Link to full text

https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9478892

ISSN

2169-3536

eISSN

2169-3536

Language

English

Author URL

http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000674123600001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a045e4b2bb1f2b747c68c720ec8913b7

Publication classification

C1 Refereed article in a scholarly journal

Publisher

Institute of Electrical and Electronics Engineers

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Categories

No categories selected

Keywords

Adaptation models Client selection Collaborative work Computational modeling Computer Science Computer Science, Information Systems Data models Data privacy Engineering Engineering, Electrical & Electronic federated learning mobile edge computing reinforcement learning Science & Technology Servers Technology Telecommunications Training 4606 Distributed computing and systems software

Licence

All Rights Reserved

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