Augmented Edge Sensing Intelligence: A Federated Learning Framework for Resource-Efficient Beam Alignment

Abstract

Low-altitude Internet of Things (IoT) networks impose stringent requirements on beam alignment due to highly dynamic environments and limited onboard resources. Conventional beam training approaches incur substantial overhead and exhibit degraded robustness under such conditions. To achieve reliable beam alignment with minimal resource consumption, this paper proposes an edge intelligence framework that integrates sensing, communication, and computation (ISCC). Specifically, a federated learning (FL) mechanism is employed to collaboratively fuse local sensor knowledge across multiple nodes. To enable beam training, integrated sensing and communication (ISAC) is leveraged as a self-supervised sensor interface to generate labeled sensing datasets directly at distributed edge nodes. Over-the-air computation (AirComp) is then adopted for local model aggregation over shared spectrum, supported by an ISAC-based dynamic transmission control scheme. Apart from designing a resource-efficient system, a joint optimization of sensing, communication and computation resources is formulated to minimize the overall energy consumption. By deriving an FL convergence bound, the optimization problem is solved via Sequential Quadratic Programming algorithm. Simulation results show that the proposed system significantly improves beam alignment accuracy in dynamic environments while reducing energy and spectrum consumption.