Fair and Private Federated Learning via Game-Theoretic Incentives

Federated learning enables multiple distributed participants to collaboratively train a machine learning model without centralizing their private data. However, participants may be reluctant to contribute data or training effort due to privacy concerns and lack of adequate incentives. In this paper, we formulate this problem as a non-cooperative game between self-interested participants, where each participant decides how much data to contribute or how much privacy to sacrifice, in order to optimize their own utility. The utility captures a trade-off between the benefit of improved global model accuracy and the cost of personal privacy loss. We prove that under mild conditions, this game admits a Nash equilibrium and that the participants can reach an equilibrium via a best response dynamics. Moreover, we introduce an incentive mechanism (e.g., reward payments) to encourage greater participation, and analyze how incentives impact the equilibrium and overall learning performance. We evaluate the proposed approach on benchmark datasets (MNIST and CIFAR-10) to demonstrate that appropriate incentives can achieve competitive accuracy and fairness among participants, compared to training without incentives.