Information Scenario-Aware Incentive Mechanisms for Federated Learning Using Game Theory and Reinforcement Learning

Abstract

Federated learning (FL), which enables collaborative training of large-scale artificial intelligence (AI) models across scattered Internet of Things (IoT) devices without exposing private data, has received a lot of attention. In FL, the performance of AI models obtained from the training is highly dependent on effective client participation. However, due to information asymmetry between the server and clients, existing incentive mechanisms often incur high incentive costs, which makes the design of cost-efficient incentive mechanisms a pressing challenge. To address the above challenge, in this article, we leverage the characteristics of market competition and propose an information scenario-aware incentive mechanism framework for FL using game theory and reinforcement learning. In particular, for the information disclosure scenario, we first formulate the server utility maximization problem as a resource pricing and purchasing game, then theoretically prove that there is at least one Nash equilibrium state for it, and finally propose a game theory-based incentive mechanism on the Nash equilibrium. Furthermore, considering the financial nature of the incentive mechanism, the clients and server are reluctant to share information for privacy concerns. Thus, for the information nondisclosure scenario, we first formulate this utility maximization problem as a Markov decision process, and then propose a multiagent reinforcement learning-based incentive mechanism to explore the near-optimal solution. Extensive experiments demonstrate that our proposed incentive mechanisms for FL can significantly improve the overall server-client utility and the training accuracy by up to 33.02% under the independent and identically distributed (i.i.d.) setting on CIFAR-100 with 50 clients.