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While privacy concerns entice connected and automated vehicles to incorporateon-board federated learning (FL) solutions, an integrated vehicle-to-everythingcommunication with heterogeneous computation power aware learning platform isurgently necessary to make it a reality. Motivated by this, we propose a novelmobility, communication and computation aware online FL platform that useson-road vehicles as learning agents. Thanks to the advanced features of modernvehicles, the on-board sensors can collect data as vehicles travel along theirtrajectories, while the on-board processors can train machine learning modelsusing the collected data. To take the high mobility of vehicles into account,we consider the delay as a learning parameter and restrict it to be less than atolerable threshold. To satisfy this threshold, the central server acceptspartially trained models, the distributed roadside units (a) perform downlinkmulticast beamforming to minimize global model distribution delay and (b)allocate optimal uplink radio resources to minimize local model offloadingdelay, and the vehicle agents conduct heterogeneous local model training. Usingreal-world vehicle trace datasets, we validate our FL solutions. Simulationshows that the proposed integrated FL platform is robust and outperformsbaseline models. With reasonable local training episodes, it can effectivelysatisfy all constraints and deliver near ground truth multi-horizon velocityand vehicle-specific power predictions.

Mobility, Communication and Computation Aware Federated Learning for Internet of Vehicles