Incentive Mechanism Design for Federated Learning: Hedonic Game Approach

Incentive mechanism design is crucial for enabling federated learning. We deal with clustering problem of agents contributing to federated learning setting. Assuming agents behave selfishly, we model their interaction as a stable coalition partition problem using hedonic games where agents and clusters are the players and coalitions, respectively. We address the following question: is there any utility allocation method ensuring a Nash-stable coalition partition? We propose the Nash-stable set and analyze the conditions of non-emptiness. Besides, we deal with the decentralized coalition partition. We formulate the problem as a non-cooperative game and prove the existence of a potential.