Incentivizing Data Contribution in Cross-Silo Federated Learning
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In cross-silo federated learning, clients (e.g., organizations) collectively train a global model using their local data. However, due to business competitions and privacy concerns, the clients tend to free-ride (i.e., not contribute enough data points) during training. To address this issue, we propose a framework where the profit/benefit obtained from the global model can be properly allocated to clients to incentivize data contribution. More specifically, we study the game-theoretical interactions among the clients under three widely used profit allocation mechanisms, i.e., linearly proportional (LP), leave-one-out (LOO), and Shapley value (SV). We consider two types of equilibrium structures: symmetric and asymmetric equilibria. We show that the three mechanisms admit an identical symmetric equilibrium structure. However, at asymmetric equilibrium, LP outperforms SV and LOO in incentivizing the clients' average data contribution. We further discuss the impact of various parameters on the clients' free-riding behaviors.
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