A Decentralized Communication-Efficient Federated Analytics Framework for Connected Vehicles

This letter presents a novel communication-efficient and decentralized approach for data analytics in connected vehicles. We extend the paradigm of federated learning (FL) to enable decentralized on-vehicle model training without a central server. To improve communication efficiency, we design a federated regularized nonlinear acceleration-based local training scheme to reduce the communication rounds and a random broadcast gossip-based mechanism to decrease the complexity per iteration. Experimental results demonstrate that our approach significantly reduces the communication cost compared to general gradient descent and momentum-based FL solutions and is promising for efficient data analytics in autonomous vehicle environments.