Joint Resource Optimization for Federated Edge Learning With Integrated Sensing, Communication, and Computation

Edge artificial intelligence (AI) is an emerging solution for pervasive intelligence service in future 6G networks, by learning machine learning (ML) models at network edge. Edge AI typically consists of three processes: sensing, communication, and computation (SC2). Edge devices first collect data samples through the sensing process, then train local models individually through the computation process, and finally update the local models periodically through the communication process to obtain the global model. Federated edge learning (FEEL) is particularly attractive for Edge AI due to its collaborative ML framework and privacy-enhancing feature. However, the research FEEL with SC2 integration remains an open question. On the one hand, there is still a lack of theoretical insight into the learning performance that is jointly influenced by the processes of SC2. On the other hand, the performance evaluation is another challenge for the proposed SC2-FEEL, which further poses the difficulties in design of efficient resource allocation. To address these issues, an SC2 integrated FEEL (SC2-FEEL) is investigated in this article, where the processes of SC2 are jointly considered and the over-the-air computation (AirComp) technique is employed for a communication-efficient model aggregation. First, theoretical analyses are conducted, which reveals both the sample sensing strategy and the AirComp-induced communication error significant affect the learning performance of SC2-FEEL. Then, we further formulate a latency and energy consumption minimization problem with learning performance guaranteed based on the theoretical results, which is mixed integer nonlinear programming (MINLP) and dynamic programming. To deal with this problem, we propose a joint SC2 resource optimization strategy with low complexity based on the block coordinate update and Lyapunov optimization framework. Extensive simulation results are provided to validate our theoretical analysis, and demonstrate the effectiveness of developed algorithm.