Smart City Gnosys

Smart city article details

Title	Privacy-Preserving Federated Learning For Transportation Mode Prediction Based On Personal Mobility Data
ID_Doc	43180
Authors	Yu F.; Xu Z.; Qin Z.; Chen X.
Year	2022
Published	High-Confidence Computing, 2, 4
DOI	http://dx.doi.org/10.1016/j.hcc.2022.100082
Abstract	Personal daily mobility trajectories/traces like Google Location Service integrates many valuable information from individuals and could benefit a lot of application scenarios, such as pandemic control and precaution, product recommendation, customized user profile analysis, traffic management in smart cities, etc. However, utilizing such personal mobility data faces many challenges since users’ private information, such as home/work addresses, can be unintentionally leaked. In this work, we build an FL system for transportation mode prediction based on personal mobility data. Utilizing FL-based training scheme, all user's data are kept in local without uploading to central nodes, providing high privacy preserving capability. At the same time, we could train accurate DNN models that is close to the centralized training performance. The resulted transportation mode prediction system serves as a prototype on user's traffic mode classification, which could potentially benefit the transportation data analysis and help make wise decisions to manage public transportation resources. © 2022
Author Keywords	Deep learning; Federated learning; Privacy

Id	Similarity	Authors	Title	Published
8067	0.895	Badu-Marfo G.; Farooq B.; Mensah D.O.; Al Mallah R.	An Ensemble Federated Learning Framework For Privacy-By-Design Mobility Behaviour Inference In Smart Cities	Sustainable Cities and Society, 97 (2023)
43196	0.881	Sun S.; Zhu Z.; Zhang X.; Song Z.	Privacy-Preserving Multi-Period Traffic Prediction Model	Lecture Notes in Networks and Systems, 1376 LNNS (2025)
43124	0.878	Naresh V.S.; Ayaaappa D.	Privacy Preserving Hierarchical Federated Learning Approach For Secure Urban Mobility Prediction	Peer-to-Peer Networking and Applications, 18, 4 (2025)
43144	0.865	Zhan Y.; Haddadi H.; Kyllo A.; Mashhadi A.	Privacy-Aware Human Mobility Prediction Via Adversarial Networks	Proceedings - 2nd International Workshop on Cyber-Physical-Human System Design and Implementation, CPHS 2022 (2022)
26385	0.864	Cavalcante I.C.; Meneguette R.I.; Torres R.H.; Mano L.Y.; Gonçalves V.P.; Ueyama J.O.; Pessin G.; Amvame Nze G.D.; Rocha Filho G.P.	Federated System For Transport Mode Detection	Energies, 15, 23 (2022)
26377	0.859	Li W.; Wang S.	Federated Meta-Learning For Spatial-Temporal Prediction	Neural Computing and Applications, 34, 13 (2022)
26308	0.854	Sepasgozar S.S.; Pierre S.	Fed-Ntp: A Federated Learning Algorithm For Network Traffic Prediction In Vanet	IEEE Access, 10 (2022)