关键词: 联盟学习, 计算卸载, 物联网（IoT）, 边缘计算

Abstract:

Smart cities, smart factories pose challenges to the performance and connectivity of Internet of Things（IoT）. The emergence of edge computing makes up for these limited capabilities. By migrating intensive computing tasks from them to Edge Nodes（EN）, IoT devices can save more energy while still maintaining their services quality. Computational offloading decisions involve collaboration and complex resource management, and they should determine it in real time based on dynamic workloads and network environments. This paper adopts the method of a simulation experiment. Maximize long-term utility by deploying deep reinforcement learning agents on both IoT devices and edge nodes, and introduce federated learning to distribute training deep reinforcement learning agents. First, build an IoT system that supports edge computing. IoT downloads existing models from EN to train; they offload intensive computing tasks to EN training; IoT uploads updated parameters to EN, and EN aggregates the parameters with the model at EN to get a new model. The cloud can get new models and aggregates at EN, and IoT can also get updated parameters from EN to apply to the device. After several iterations, IoT can get close to centralized training performance and reduce the transmission cost between IoT devices and edge nodes. Experiments verify the effectiveness of decision-making schemes and federated learning in dynamic IoT environment.

Key words: federated learning, computation offloading, Internet of Things（IoT）, edge computing