A Concurrent Federated Reinforcement Learning for IoT Resources Allocation With Local Differential Privacy

Resource allocation in an edge-based Internet of Things (IoT) systems can be a challenging task, especially when the system contains many devices. Hence, in recent years, scholars have devoted some attention to designing different resource allocation strategies. Among these strategies, reinforcement learning is considered to be one of the best methods for maximizing the efficiency of resource allocation schemes. In a typical reinforcement learning scheme, the edge hosts would be required to upload their local parameters to a central server. However, this process has privacy implications given some of the data processed by the edge hosts is likely to be highly sensitive. To tackle this privacy issue, we developed a concurrent joint reinforcement learning method based on local differential privacy. Our approach allows the edge host to add noise during local training to preserve privacy, and to make joint decisions with the central server to devise an optimal resource allocation strategy. Experiments show that this approach yields high performance while preserving the privacy of the edge hosts.