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Autonomous vehicles (AVs) must interact with a diverse set of human driversin heterogeneous geographic areas. Ideally, fleets of AVs should sharetrajectory data to continually re-train and improve trajectory forecastingmodels from collective experience using cloud-based distributed learning. Atthe same time, these robots should ideally avoid uploading raw driverinteraction data in order to protect proprietary policies (when sharinginsights with other companies) or protect driver privacy from insurancecompanies. Federated learning (FL) is a popular mechanism to learn models incloud servers from diverse users without divulging private local data. However,FL is often not robust -- it learns sub-optimal models when user data comesfrom highly heterogeneous distributions, which is a key hallmark of human-robotinteractions. In this paper, we present a novel variant of personalized FL tospecialize robust robot learning models to diverse user distributions. Ouralgorithm outperforms standard FL benchmarks by up to 2x in real user studiesthat we conducted where human-operated vehicles must gracefully merge laneswith simulated AVs in the standard CARLA and CARLO AV simulators.

Personalized Federated Learning of Driver Prediction Models for Autonomous Driving