Resource Scheduling Based on Reinforcement Learning Based on Federated Learning

The emergence of edge computing makes up for the limited capacity of devices. By migrating intensive computing tasks from them to edge nodes (EN), we can save more energy while still maintaining the quality of service. Computing offload decision involves collaboration and complex resource management. It should be determined in real time according to dynamic workload and network environment. The simulation experiment method is used to maximize the long-term utility by deploying deep reinforcement learning agents on IOT devices and edge nodes, and the alliance learning is introduced to distribute the deep reinforcement learning agents. First, build the Internet of things system supporting edge computing, download the existing model from the edge node for training, and unload the intensive computing task to the edge node for training; upload the updated parameters to the edge node, and the edge node aggregates the parameters with the The model at the edge node can get a new model; the cloud can get a new model at the edge node and aggregate, and can also get updated parameters from the edge node to apply to the device.