Measuring Participant Contributions in Decentralized Federated Learning

This paper introduces new methods for measuring participant contributions in decentralized federated learning (DFL), where clients exchange models directly without a central server. The authors propose DFL-Shapley, an extension of the Shapley value for DFL, and its approximation DFL-MR, both validated through experiments to effectively assess contributions in decentralized settings.