  Urban Air Mobility (UAM) expands vehicles from the ground to the near-ground
space, envisioned as a revolution for transportation systems. Comprehensive
scene perception is the foundation for autonomous aerial driving. However, UAM
encounters the intelligent perception challenge: high perception learning
requirements conflict with the limited sensors and computing chips of flying
cars. To overcome the challenge, federated learning (FL) and other
collaborative learning have been proposed to enable resource-limited devices to
conduct onboard deep learning (DL) collaboratively. But traditional
collaborative learning like FL relies on a central integrator for DL model
aggregation, which is difficult to deploy in dynamic environments. The fully
decentralized learning schemes may be the intuitive solution while the
convergence of distributed learning cannot be guaranteed. Accordingly, this
paper explores reconfigurable intelligent surfaces (RIS) empowered distributed
learning, taking account of topological attributes to facilitate the learning
performance with convergence guarantee. We propose several FL topological
criteria for optimizing the transmission delay and convergence rate by
exploiting the Laplacian matrix eigenvalues of the communication network.
Subsequently, we innovatively leverage the RIS link modification ability to
remold the current network according to the proposed topological criteria. This
paper rethinks the functions of RIS from the perspective of the network layer.
Furthermore, a deep deterministic policy gradient-based RIS phase shift control
algorithm is developed to construct or deconstruct the network links
simultaneously to reshape the communication network. Simulation experiments are
conducted over MobileNet-based multi-view learning to verify the efficiency of
the distributed FL framework.
