Federated learning on non-IID and long-tailed data via dual-decoupling
Federated learning on non-IID and long-tailed data via dual-decoupling
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Publisher
Hangzhou: Zhejiang University Press
Journal title
Language
English
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Publisher
Hangzhou: Zhejiang University Press
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Scope and Contents
Contents
Federated learning (FL), a cutting-edge distributed machine learning training paradigm, aims to generate a global model by collaborating on the training of client models without revealing local private data. The cooccurrence of non-independent and identically distributed (non-IID) and long-tailed distribution in FL is one challenge that substantial...
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Full title
Federated learning on non-IID and long-tailed data via dual-decoupling
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TN_cdi_proquest_journals_3065609371
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_3065609371
Other Identifiers
ISSN
2095-9184
E-ISSN
2095-9230
DOI
10.1631/FITEE.2300284
