Covert Communications in Air-Ground Integrated Urban Sensing Networks Enhanced by Federated Learning

Urban sensing is a rapidly growing field that involves gathering real-time data from urban environments. These smart sensing systems are pivotal in enhancing urban planning, resource management, and overall quality of life in cities. However, in the context of data collection and network congestion, nonnegligible latency can be experienced in urban environments. Recently, federated learning (FL) has emerged as an attractive approach to address network latency concerns. FL achieves distributing data processing tasks in urban sensing by uploading training results rather than raw data to a central server. In light of this, our article proposes a wireless sensor air-ground integrated FL (AGIFL) network with covert communication. The AGIFL system utilizes a high-altitude platform (HAP) as a parameter server to provide mobility and sustainability of urban sensors and a friendly jammer unmanned aerial vehicle (UAV) to enhance information security. Our main objective is to minimize FL latency for the urban sensor network and ensure secure transmission. To achieve this, we jointly optimize the local training accuracy, interference power, and transmit power of FL sensors. To tackle the nonconvexity of the problem, we decompose the original nonconvex problem into subproblems and solve it using successive convex approximation (SCA) with alternating iterations. Simulation results show that the proposed scheme reduces latency in urban sensor networks significantly and ensures the secure transmission of information compared to benchmarks.