Optimal User Selection for High-Performance and Stabilized Energy-Efficient Federated Learning Platf...
Optimal User Selection for High-Performance and Stabilized Energy-Efficient Federated Learning Platforms
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Publisher
Basel: MDPI AG
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Language
English
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Publisher
Basel: MDPI AG
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Scope and Contents
Contents
Federated learning-enabled edge devices train global models by sharing them while avoiding local data sharing. In federated learning, the sharing of models through communication between several clients and central servers results in various problems such as a high latency and network congestion. Moreover, battery consumption problems caused by loca...
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Full title
Optimal User Selection for High-Performance and Stabilized Energy-Efficient Federated Learning Platforms
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TN_cdi_proquest_journals_2437273103
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2437273103
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ISSN
2079-9292
E-ISSN
2079-9292
DOI
10.3390/electronics9091359
