Secure FLOATING - Scalable Federated Learning Framework for Real-time Trust in Mobility Data using Secure Multi-Party Computation and Blockchain

The safety of Connected and Autonomous Vehicles (CAVs), Micromobility devices (e-scooter, e-bikes) and smartphone users rely on trusting the trajectory data they generate for navigation around each other. Real-time verification of mobility data from these devices without compromising privacy is needed as malicious data used for navigation could be deadly, especially for vulnerable road users. In this paper, we propose Secure-FLOATING, a scalable framework leveraging federated learning and blockchain for nearby nodes to coordinate and learn to trust mobility data from nearby devices and store this information via consensus on a tamper-proof distributed ledger. We employ lightweight Secure Multi-party computation (SMPC) with reduced messages exchanges to preserve privacy of the users and ensure data validation in real-time. Secure-FLOATING is evaluated using realistic trajectories for up to 8, 000 nodes (vehicles, micromobility devices, and pedestrians) in New York City, and it shows to achieve lower delays and overhead, thereby accurately validating each others' mobility data in a scalable manner, with up to 75% successful endorsement for as high as 50% attacker penetration.