Edge Federated Optimization for Heterogeneous Data
Edge Federated Optimization for Heterogeneous Data
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Publisher
Basel: MDPI AG
Journal title
Language
English
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Publisher
Basel: MDPI AG
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Scope and Contents
Contents
This study focuses on optimizing federated learning in heterogeneous data environments. We implement the FedProx and a baseline algorithm (i.e., the FedAvg) with advanced optimization strategies to tackle non-IID data issues in distributed learning. Model freezing and pruning techniques are explored to showcase the effective operations of deep lear...
Alternative Titles
Full title
Edge Federated Optimization for Heterogeneous Data
Authors, Artists and Contributors
Author / Creator
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Primary Identifiers
Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_fa757d11e3fc423fa11721cff163eacc
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_fa757d11e3fc423fa11721cff163eacc
Other Identifiers
ISSN
1999-5903
E-ISSN
1999-5903
DOI
10.3390/fi16040142
