FLOWER: Federated Learning based Zero-Trust Consensus Protocol for Real-time Trajectory En...

Connected and Autonomous Vehicles (CAVs) can share their future trajectories with nodes around them as the intended navigation path, for nearby nodes to avoid crashing into them. However, trust must be established on the shared trajectories where the nearby nodes can verify the truthfulness of the shared trajectories in an efficient and timely manner. This paper proposes FLOWER, a federated learning based approach as a distributed zero trust security protocol for nearby nodes to verify the trajectories shared among CAVs by employing a machine learning algorithm to predict the corresponding future trajectories and verify the truthfulness of the data shared by the CAV via a blockchain based consensus. We employ several machine learning algorithms including transformer models on realistic trajectories from New York City to achieve this and results have shown that simple time series algorithms (RNN, LSTM, GRUs) achieved similar performance without additional complexity for real-time verification of CAV trajectories.

FLOWER: Federated Learning based Zero-Trust Consensus Protocol for Real-time Trajectory Endorsement in CAVs

Bo Sullivan, Synnove Svendsen, Junaid Ahmed Khan, Western Washington University