Federated learning for digital twin applications: a privacy-preserving and low-latency approach.

ABSTRACT: The digital twin (DT) concept has recently gained widespread application for mapping the state of physical entities, enabling real-time analysis, prediction, and optimization, thereby enhancing the management and control of physical systems. However, when sensitive information is extracted from physical entities, it faces potential leakage risks, as DT service providers are typically honest yet curious. Federated learning (FL) offers a new distributed learning paradigm that protects privacy by transmitting model updates from edge servers to local devices, allowing training on local datasets. Nevertheless, the training parameters communicated between local mobile devices and edge servers may contain raw data that malicious adversaries could exploit. Furthermore, variations in mapping bias a

SUBMITTER: Li J 

PROVIDER: S-EPMC12453653 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature