A dynamic incentive and reputation mechanism for energy-efficient federated learning in 6G

Abstract

As 5G becomes commercial, researchers have turned attention toward the Sixth-Generation (6G) network with the vision of connecting intelligence in a green energy-efficient manner. Federated learning triggers an upsurge of green intelligent services such as resources orchestration of communication infrastructures while preserving privacy and increasing communication efficiency. However, designing effective incentives in federated learning is challenging due to the dynamic available clients and the correlation between clients' contributions during the learning process. In this paper, we propose a dynamic incentive and reputation mechanism to improve energy efficiency and training performance of federated learning. The proposed incentive based on the Stackelberg game can timely adjust optimal energy consumption with changes in available clients during federated learning. Meanwhile, clients’ contributions in reputation management are formulated based on the cooperative game to capture the correlation between tasks, which satisfies availability, fairness, and additivity. The simulation results show that the proposed scheme can significantly motivate high-performance clients to participate in federated learning and improve the accuracy and energy efficiency of the federated learning model.

Keywords

• Federated learning
• Incentive mechanism
• Reputation management
• Cooperative game
• Stackelberg game
• Green communication