FLamma claims to balance fairness and accuracy in federated learning via a Stackelberg game with an adaptive decay factor, but the theory has derivation errors and the experiments use fixed local epochs.
Incentive Mechanism Design for Federated Learning: Hedonic Game Approach
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abstract
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 a family of hedonic games ensuring a Nash-stable coalition partition? We propose the Nash-stable set which determines the family of hedonic games possessing at least one Nash-stable partition, and analyze the conditions of non-emptiness of the Nash-stable set. Besides, we deal with the decentralized clustering. We formulate the problem as a non-cooperative game and prove the existence of a potential game.
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Incentive-Compatible Federated Learning with Stackelberg Game Modeling
FLamma claims to balance fairness and accuracy in federated learning via a Stackelberg game with an adaptive decay factor, but the theory has derivation errors and the experiments use fixed local epochs.