  The Internet of Vehicles (IoV) emerges as a pivotal component for autonomous
driving and intelligent transportation systems (ITS), by enabling low-latency
big data processing in a dense interconnected network that comprises vehicles,
infrastructures, pedestrians and the cloud. Autonomous vehicles are heavily
reliant on machine learning (ML) and can strongly benefit from the wealth of
sensory data generated at the edge, which calls for measures to reconcile model
training with preserving the privacy of sensitive user data. Federated learning
(FL) stands out as a promising solution to train sophisticated ML models in
vehicular networks while protecting the privacy of road users and mitigating
communication overhead. This paper examines the federated optimization of the
cutting-edge YOLOv7 model to tackle real-time object detection amid data
heterogeneity, encompassing unbalancedness, concept drift, and label
distribution skews. To this end, we introduce FedPylot, a lightweight MPI-based
prototype to simulate federated object detection experiments on
high-performance computing (HPC) systems, where we safeguard server-client
communications using hybrid encryption. Our study factors in accuracy,
communication cost, and inference speed, thereby presenting a balanced approach
to the challenges faced by autonomous vehicles. We demonstrate promising
results for the applicability of FL in IoV and hope that FedPylot will provide
a basis for future research into federated real-time object detection. The
source code is available at https://github.com/cyprienquemeneur/fedpylot.
