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Federated Learning rests on the notion of training a global modeldistributedly on various devices. Under this setting, users' devices performcomputations on their own data and then share the results with the cloud serverto update the global model. A fundamental issue in such systems is toeffectively incentivize user participation. The users suffer from privacyleakage of their local data during the federated model training process.Without well-designed incentives, self-interested users will be unwilling toparticipate in federated learning tasks and contribute their private data. Tobridge this gap, in this paper, we adopt the game theory to design an effectiveincentive mechanism, which selects users that are most likely to providereliable data and compensates for their costs of privacy leakage. We formulateour problem as a two-stage Stackelberg game and solve the game's equilibrium.Effectiveness of the proposed mechanism is demonstrated by extensivesimulations.

Trading Data For Learning: Incentive Mechanism For On-Device Federated Learning