FLOATING: Federated Learning for Optimized Automated Trajectory Information StoriNG on Blockchain

Junaid Ahmed Khan, Weiyi Wang, Kaan Ozbay

Abstract: Trajectory data from mobile, micro-mobility devices (e-scooter, e-bikes, etc) and vehicles need validation regarding its trustworthiness for utility in different applications. Sharing of false trajectories from compromised devices can lead to potentially fatal consequences for safety-related applications. There is no scalable method to assess the truthfulness of trajectory data in real-time, therefore, this paper proposes FLOATING, leveraging federated reinforcement learning to automate trajectory validation on a private-by-design blockchain. FLOATING employs a three-tier consensus process for nodes in each others vicinity to endorse trajectories in real-time. We evaluate FLOATING using NS-3 and it shows to achieve lower delays and network overhead for a network size of up to 50 nodes participating in the consensus, while reducing network resource utilization by 10 times.