HFedMS: Heterogeneous Federated Learning with Memorable Data Semantics in Industrial Metaverse-Reference-Cited by-同舟云学术

HFedMS: Heterogeneous Federated Learning with Memorable Data Semantics in Industrial Metaverse

Author:

Zeng Shenglai¹,Li Zonghang²^ORCID,Yu Hongfang²^ORCID,Zhang Zhihao¹,Luo Long²^ORCID,Li Bo³^ORCID,Niyato Dusit⁴^ORCID

Affiliation:

1. Yingcai Honors College and School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China

2. School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China

3. Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, China

4. School of Computer Science and Engineering, Nanyang Technological University, Singapore

Funder

National Natural Science Foundation of China

Sichuan Province Selected Funding for Postdoctoral Research Projects

PCL Future Greater-Bay Area Network Facilities for Large-Scale Experiments and Applications

Open Research Projects of Zhejiang Lab

Nanyang Technological University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Software

Link

Reference61 articles.

2. Constrained K-means clustering;bradley,2000

4. Communication-efficient learning of deep networks from decentralized data;mcmahan;Proc 20th Int Conf Artif Intell Statist,2017

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