  Over-the-air federated learning (OTA-FL) exploits the inherent superposition
property of wireless channels to integrate the communication and model
aggregation. Though a naturally promising framework for wireless federated
learning, it requires care to mitigate physical layer impairments. In this
work, we consider a heterogeneous edge-intelligent network with different edge
device resources and non-i.i.d. user dataset distributions, under a general
non-convex learning objective. We leverage the Reconfigurable Intelligent
Surface (RIS) technology to augment OTA-FL system over simultaneous time
varying uplink and downlink noisy communication channels under imperfect CSI
scenario. We propose a cross-layer algorithm that jointly optimizes RIS
configuration, communication and computation resources in this general
realistic setting. Specifically, we design dynamic local update steps in
conjunction with RIS phase shifts and transmission power to boost learning
performance. We present a convergence analysis of the proposed algorithm, and
show that it outperforms the existing unified approach under heterogeneous
system and imperfect CSI in numerical results.
