Reliable Federated Learning Based Traffic Crowdsourcing in the Presence of Adversarial Users.

The extended computing power and ubiquity of smartphones offer tremendous opportunities for mobile crowdsourcing-assisted traffic information collection. Due to the dynamic and unsupervised nature of mobile sensing nodes, data quality is a major concern, and reputation systems are often employed to mitigate the impact of unreliable data sources. In this paper, we propose using federated learning with iterative filtering in traffic information crowdsourcing to discover the truth, calculate the reputation of all mobile sensing nodes, and recover the truth about traffic conditions. Our simulation results show that the proposed approach is robust to collusion attacks, provides better accuracy, and converges to the truth quickly.