Federated Learning With Experience-Driven Model Migration in Heterogeneous Edge Networks-Reference-Cited by-同舟云学术

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Federated Learning With Experience-Driven Model Migration in Heterogeneous Edge Networks

Published:2024-08 Issue:4 Volume:32 Page:3468-3484
ISSN:1063-6692
Container-title:IEEE/ACM Transactions on Networking
language:
Short-container-title:IEEE/ACM Trans. Networking

Author:

Liu Jianchun¹^ORCID,Wang Shilong¹,Xu Hongli¹^ORCID,Xu Yang¹^ORCID,Liao Yunming¹^ORCID,Huang Jinyang²^ORCID,Huang He³^ORCID

Affiliation:

1. School of Computer Science and Technology, University of Science and Technology of China, Anhui, Hefei, China

2. S2AC Laboratory, the Key Laboratory of Knowledge Engineering with Big Data, and the School of Computer and Information, Hefei University of Technology, Anhui, Hefei, China

3. School of Computer Science and Technology, Soochow University, Suzhou, China

Funder

National Science Foundation of China

Jiangsu Province Science Foundation for Youths

Fundamental Research Funds for the Central Universities

University of Science and Technology of China (USTC) Research Funds of the Double First-Class Initiative

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/90/10640200/10508334.pdf?arnumber=10508334

Reference56 articles.

1. Enhancing Federated Learning with Intelligent Model Migration in Heterogeneous Edge Computing

2. Mobile Edge Computing: A Survey on Architecture and Computation Offloading

3. A Survey on Mobile Edge Computing: The Communication Perspective

4. Neural machine translation by jointly learning to align and translate;Bahdanau;arXiv:1409.0473,2014

5. Opinion mining and sentiment analysis;Bakshi

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