A New Look and Convergence Rate of Federated Multitask Learning With Laplacian Regularization-Reference-Cited by-同舟云学术

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A New Look and Convergence Rate of Federated Multitask Learning With Laplacian Regularization

Published:2022 Issue: Volume: Page:1-11
ISSN:2162-237X
Container-title:IEEE Transactions on Neural Networks and Learning Systems
language:
Short-container-title:IEEE Trans. Neural Netw. Learning Syst.

Author:

Dinh Canh T.¹^ORCID,Vu Tung T.²^ORCID,Tran Nguyen H.¹^ORCID,Dao Minh N.³^ORCID,Zhang Hongyu⁴^ORCID

Affiliation:

1. School of Computer Science, The University of Sydney, Sydney, NSW, Australia

2. Department of Electrical Engineering (ISY), Linköping University, SE-, Sweden

3. School of Science, RMIT University, Melbourne, VIC, Australia

4. The University of Newcastle, Callaghan, NSW, Australia

Funder

Australian Research Council’s Discovery Projects

public grant as part of the Investissement d’avenir project, reference

LabEx LMH

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Computer Networks and Communications,Computer Science Applications,Software

Link

http://xplorestaging.ieee.org/ielx7/5962385/6104215/09975151.pdf?arnumber=9975151

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