Leveraging Foundation Models to Improve Lightweight Clients in Federated Learning
Leveraging Foundation Models to Improve Lightweight Clients in Federated Learning
About this item
Full title
Author / Creator
Publisher
Ithaca: Cornell University Library, arXiv.org
Journal title
Language
English
Formats
Publication information
Publisher
Ithaca: Cornell University Library, arXiv.org
Subjects
More information
Scope and Contents
Contents
Federated Learning (FL) is a distributed training paradigm that enables clients scattered across the world to cooperatively learn a global model without divulging confidential data. However, FL faces a significant challenge in the form of heterogeneous data distributions among clients, which leads to a reduction in performance and robustness. A rec...
Alternative Titles
Full title
Leveraging Foundation Models to Improve Lightweight Clients in Federated Learning
Authors, Artists and Contributors
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_proquest_journals_2890526997
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2890526997
Other Identifiers
E-ISSN
2331-8422
