Incentive Mechanisms in Federated Learning and A Game-Theoretical Approach

Federated learning (FL) represents a new machine paradigm, utilizing various resources from participants to collaboratively train global model without exposing the privacy of training data. The performance critically depends on provided by and their active participation. Hence, it is essential enable more actively contribute valuable in FL. In this article, we present survey incentive mechanisms for We identify problem, outline its framework, categorically discuss state-of-the-art Shapley value, Stackelberg game, auction, contract, reinforcement learning. addition, propose three multi-dimensional game-theoretical models study economical behaviors demonstrate applicability cross-silo FL scenarios.