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.

翻译：城市空中交通将交通工具从地面拓展至近地空间，被视为交通系统的革命性变革。全面场景感知是自主空中驾驶的基础，然而城市空中交通面临智能感知挑战：高感知学习需求与飞行汽车有限的传感器和计算芯片存在矛盾。为克服这一挑战，联邦学习及其他协同学习方法被提出，使资源受限设备能够协作进行机载深度学习。但传统协同学习（如联邦学习）依赖中央集成器进行深度学习模型聚合，难以在动态环境中部署。完全去中心化学习方案可能是直观解决方案，但分布式学习的收敛性无法保证。为此，本文探索可重构智能表面赋能的分布式学习，考虑拓扑属性以促进具有收敛保证的学习性能。我们提出若干联邦学习拓扑准则，通过利用通信网络的拉普拉斯矩阵特征值优化传输时延与收敛速率。随后，创新性地利用RIS链路修改能力，根据所提拓扑准则重塑当前网络。本文从网络层视角重新审视RIS的功能。进一步，开发了基于深度确定性策略梯度的RIS相位调整算法，通过同时构建或解构网络链路来重塑通信网络。基于MobileNet的多视角学习仿真实验验证了分布式联邦学习框架的有效性。