Resource Allocation in IoT Edge Computing via Concurrent Federated Reinforcement Learning
Tianqing Zhu, Wei Zhou, Dayong Ye, Zishuo Cheng, Jin Li
Abstract
Resource allocation is a fundamental research issue in IoT edge computing, and reinforcement learning is fast becoming a common solution. The majority of the current techniques involve decision makers who determine how and where resources should be distributed. In a standard cloud system, this decision maker is a central server. In an edge system, the decision maker is an edge host. Both approaches have drawbacks. Edge hosts do not always have access to enough global information to create the most optimal resource allocation strategy. Central servers do but at the cost of privacy. A solution is needed that can do both. This article, therefore, presents a novel resource allocation method called concurrent federated reinforcement learning. The scheme inherits the privacy protection of federated learning, the complex problem solving power of reinforcement learning and adds concurrency in the form of joint decision making so the resource allocation strategies work to the benefit of the global system. The experiments demonstrate that the approach provides the state-of-the-art performance in system-wide utility, speed of task completion, and resource utilization.