A Survey of Personalized and Incentive Mechanisms for Federated Learning

Abstract

Federated learning (FL) provides a higher privacy guarantee for data sharing in a multi-party computation environment. However, how to invite participants to federated training if they already have a self-sanitized dataset? What is more, FL can not be directly applied to Non-IID data, and the global model can not meet the different feature requirements of clients. Personalized and incentive mechanisms are very necessary to build a good learning environment for FL. However, there has been little discussion about personalized and incentive mechanisms schemes so far, while more attention is focused on the optimization, efficiency and effectiveness improvement, and security aspects. Thus, in this paper, we make a review of personalized and incentive mechanisms of federated learning with different techniques.