A Differential Evolution Offloading Strategy for Latency and Privacy Sensitive Tasks with Federated Local-edge-cloud Collaboration

Due to an explosive growth in mobile devices and the rapid evolution of wireless communication technologies, local-edge-cloud computing is becoming an attractive solution for providing a higher-quality service by exploiting the multi-computation power of mobile devices, edge servers and cloud. However, as the tasks are latency and privacy sensitive, highly credible task offloading becomes a crucial problem in a local-edge-cloud orchestrated computing system. In this paper, we study the computation offloading problem for latency and privacy sensitive tasks in a hierarchical local-edge-cloud network by using federated learning method. Our goal is to minimize the operational time of latency-sensitive tasks requested by mobile devices that have data privacy concerns, while each task can be executed under local, edge or cloud computing mode with no need to rely on privacy data. We first build system models to analyze the latency incurred under different computing modes, and then develop a constrained optimization problem to minimize the latency consumed by the federated offloading collaboration. A Hierarchical Federated Averaging method based on Differential Evolution algorithm (HierFAVG-DE) is proposed for solving the problem in-hand, and extensive simulations are conducted to verify the superiority of our approach.