  Realizing human-like perception is a challenge in open driving scenarios due
to corner cases and visual occlusions. To gather knowledge of rare and occluded
instances, federated learning assisted connected autonomous vehicle (FLCAV) has
been proposed, which leverages vehicular networks to establish federated deep
neural networks (DNNs) from distributed data captured by vehicles and road
sensors. Without the need of data aggregation, FLCAV preserves privacy while
reducing communication costs compared with conventional centralized learning.
However, it is challenging to determine the network resources and road sensor
placements for multi-stage training with multi-modal datasets in multi-variant
scenarios. This article presents networking and training frameworks for FLCAV
perception. Multi-layer graph resource allocation and vehicle-road contrastive
sensor placement are proposed to address the network management and sensor
deployment problems, respectively. We also develop CarlaFLCAV, a software
platform that implements the above system and methods. Experimental results
confirm the superiority of the proposed techniques compared with various
benchmarks.
