Personalized Federated Learning With Model-Contrastive Learning for Multi-Modal User Modeling in Human-Centric Metaverse
Shiga University · RIKEN Center for Advanced Intelligence Project · +6 more institutions
Abstract
With the flourish of digital technologies and rapid development of 5G and beyond networks, Metaverse has become an increasingly hotly discussed topic, which offers users with multiple roles for diversified experience interacting with virtual services. How to capture and model users’ multi-platform or cross-space data/behaviors become essential to enrich people with more realistic and immersed experience in Metaverse-enabled smart applications over 5G and beyond networks. In this study, we propose a Personalized Federated Learning with Model-Contrastive Learning (PFL-MCL) framework, which may efficiently enhance the communication and interaction in human-centric Metaverse environments by making use of the…
Citation impact
- FWCI
- 42.25
- Percentile
- 100%
- References
- 39
Authors
8Topics & keywords
- Computer science
- Modal
- Metaverse
- Human–computer interaction
- Artificial intelligence
- Virtual reality