PPDF-FedTMI: A Federated Learning-based Transport Mode Inference Model with Privacy-Preserving Data Fusion

Abstract

Mobile Crowd-Sensing (MCS) represents a distributed data fusion system that aggregates diverse sources to enhance inference capabilities beyond any single input. As an essential application in MCS, intelligent transportation system deploys a large number of Transport Mode Inference (TMI) models. However, robust TMI models necessitates large trajectories datasets, engendering significant privacy risks. Existing solutions fail to adequately preserve trajectory privacy in collaborative MCS. To enable secure distributed modeling while safeguarding user anonymity, a Federated Learning-based Transport Mode Inference Model with Privacy-Preserving Data Fusion (PPDF-FedTMI) is proposed in this paper. Firstly, federated learning is incorporated into the TMI architecture to mitigate isolated data silos in MCS. Subsequently, a fuzzy fusion algorithm leverages fuzzy clustering of labels to facilitate stable and effective training under non-independent and non-identically distributed decentralized data. Furthermore, a local differential privacy-based joint training technique is devised to enhance gradient privacy, preventing exposure of original user data through model updates. Finally, the experiments demonstrate PPDF-FedTMI provides superior inference accuracy and effective privacy-preserving compared to current models.