Federated Trajectory Similarity Learning with Privacy-Preserving Clustering

Movement trajectory similarity computation is important when supporting functionalities such as outlier detection and prediction that may, in turn, fuel a variety of transportation-related applications. Recent trajectory similarity learning solutions often assume that trajectories are available at a central location. Yet, we are witnessing the decentralized collection of increasingly massive volumes of trajectories due to the deployment of edge devices. To enable decentralized training and improved privacy, we propose a federated trajectory similarity learning framework that features privacy-preserving clustering based on a client-server architecture. The framework encompasses local, client-side trajectory preprocessing and representation learning. This is combined with a novel privacy-preserving clustering mechanism that ensures consistent model updates between clients and the server, thus alleviating the effects of trajectory heterogeneity across clients. In addition, the framework features a hierarchical central aggregation mechanism that supports clustered federated learning. Experiments on real data offer evidence that the effectiveness of the proposed framework performs as intended.