Effective Cost Sharing for Federated Learning Framework

Abstract

In a federated learning setup the quality of the datasets of the participants may vary greatly. Sharing costs associated with training a model equally across all participants may not appeal participants with high quality data. In this work we implemented Shapley values to compute the contributions of participants in a federated learning framework. We evaluate different methods to compute the contributions and propose methods to share costs based on said contributions. Furthermore we implemented Shapley values for features of participants.