Collaborative Federated Learning in Mobile Vehicle Clouds for Online Ride-Hailing Passenger Zones Recommendation

Recommendations for ride-hailing zones are crucial for matching drivers with passengers efficiently, improving mobility, and managing traffic effectively. However, current recommendation methods based on centralized machine learning suffer from privacy concerns due to the need for data sharing and aggregation. Furthermore, the lack of personalized results tailored to individual regions or drivers within a single architecture often leads to spatial supply imbalances in ride-hailing services. To address these challenges, we propose in this work Federated Geo-Aware Matrix Factorization (FedGeoLMF), a framework for vehicular-cloud collaborative federated learning that identifies vehicles with similar passenger patterns, aiming to enhance the accuracy and efficiency of recommendations by improving local nodes for federated learning. At local nodes, federated matrix factorization algorithms based on historical trajectory data of moving vehicles and fused geographical information to achieve local learning and recommendations are designed. Additionally, we introduce a communication mechanism tailored for federated learning models exchanged between servers and mobile vehicles, employing the Named Data Networking (NDN) protocol that streamlines parameter uploading and downloading processes, unveiling akin patterns within local federated matrix decomposition outcomes by harnessing the NDN routing algorithm, delivering personalized collaborative recommendations while upholding the confidentiality of user-sensitive data. Finally, we design an online recommendation method based on driver location and reachability matrices to recommend optimal ride-hailing zones for drivers in different locations. Experimental results demonstrate that the proposed method outperforms current baseline models in terms of accuracy and training efficiency and can efficiently provide personalized ride recommendations while ensuring privacy in the ride-hailing service domain.