Incentive Mechanism Design for Federated Learning: Hedonic Game Approach
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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.
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