YoungHwan Jeong, Won-gi Choi, Hyoseon Kye, JeeHyeong Kim, Min-hwan Song, Sang-shin Lee, Journal of Internet Computing and Services, Vol. 25, No. 4, pp. 23-37, Aug. 2024
10.7472/jksii.2024.25.4.23, Full Text:
Keywords: Digital Twin, Federated learning, Vector database, training optimization, Privacy, similarity search

Abstract

Digital twin is an M&S (Modeling and Simulation) technology designed to solve or optimize problems in the real world by replicating physical objects in the real world as virtual objects in the digital world and predicting phenomena that may occur in the future through simulation. Digital twins have been elaborately designed and utilized based on data collected to achieve specific purposes in large-scale environments such as cities and industrial facilities. In order to apply this digital twin technology to real life and expand it into user-customized service technology, practical but sensitive issues such as personal information protection and personalization of simulations must be resolved. To solve this problem, this paper proposes a federated learning-based accelerated client training method (FACTS) for personalized digital twins. The basic approach is to use a cluster-driven federated learning training procedure to protect personal information while simultaneously selecting a training model similar to the user and training it adaptively. As a result of experiments under various statistically heterogeneous conditions, FACTS was found to be superior to the existing FL method in terms of training speed and resource efficiency.

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