Personalized Federated Learning With Model-Contrastive Learning for Multi-Modal User Modeling in Human-Centric Metaverse-Reference-Cited by-同舟云学术

Personalized Federated Learning With Model-Contrastive Learning for Multi-Modal User Modeling in Human-Centric Metaverse

Author:

Zhou Xiaokang¹,Yang Qiuyue²,Zheng Xuzhe³^ORCID,Liang Wei²^ORCID,Wang Kevin I-Kai⁴^ORCID,Ma Jianhua⁵^ORCID,Pan Yi⁶^ORCID,Jin Qun⁷^ORCID

Affiliation:

1. Faculty of Data Science, Shiga University, Hikone, Japan

2. Changsha Social Laboratory of Artificial Intelligence, Hunan University of Technology and Business, Changsha, China

3. School of Frontier Crossover Studies, Hunan University of Technology and Business, Changsha, China

4. Department of Electrical, Computer and Software Engineering, The University of Auckland, Auckland, New Zealand

5. Faculty of Computer and Information Sciences, Hosei University, Tokyo, Chiyoda, Japan

6. Department of Computer Science, Georgia State University, Atlanta, GA, USA

7. Faculty of Human Sciences, Waseda University, Tokorozawa, Japan

Funder

Grants-in-Aid for Scientific Research (C) from Japan Society for the Promotion of Science

National Natural Science Foundation of China

Waseda University Grants for Special Research Projects

Shenzhen Science and Technology Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

Reference37 articles.

3. Communication-efficient on-device machine learning: Federated distillation and augmentation under non-IID private data;Jeong;arXiv:1811.11479,2018

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