A Game-Theoretic Framework for Incentive Mechanism Design in Federated Learning

Abstract

Federated learning (FL) has great potential for coalescing isolated data islands. It enables privacy-preserving collaborative model training and addresses security and privacy concerns. Besides booming technological breakthroughs in this field, for better commercialization of FL in the business world, we also need to provide sufficient monetary incentives to data providers. The problem of FL incentive mechanism design is therefore proposed to find out the optimal organization and payment structure for the federation. This problem can be tackled by game theory.

In this chapter, we set up a research framework for reasoning about FL incentive mechanism design. We introduce key concepts and their mathematical notations specified under the FML environment, hereby proposing a precise definition of the FML incentive mechanism design problem. Then, we break down the big problem into a demand-side problem and a supply-side problem. Based on different settings and objectives, we provide a checklist for FL practitioners to choose the appropriate FL incentive mechanism without deep knowledge in game theory.

As examples, we introduce the Crémer-McLean mechanism to solve the demand-side problem and present a VCG-based mechanism, PVCG, to solve the demand-side problem. These mechanisms both guarantee truthfulness, i.e., they encourage participants to truthfully report their private information and offer all their data to the federation. Crémer-McLean mechanism, together with PVCG, attains allocative efficiency, individual rationality, and weak budget balancedness at the same time, easing the well-known tension between these objectives in the mechanism design literature.

Supported by LogiOcean Co., Ltd.