An Incentivized Federated Learning Model Based on Contract Theory

In view of the fact that there is rare research on the incentive mechanism design in decentralized federated learning, and the existing incentive mechanisms for federated learning are seldom based on the global model effect, an incentive mechanism based on contract theory, is added into decentralized federated learning and a new incentivized federated learning model is proposed. A blockchain and an InterPlanetary File System (IPFS) are used to replace the central server of traditional federated learning for model parameter storage and distribution, based on which a contract publisher is responsible for contract formulation and distribution, and each federated learning participant chooses to sign a contract based on its local data quality. The contract publisher evaluates each local training model after each round of local training and issues a reward based on the agreed-upon conditions in the contract. The global model aggregation also aggregates model parameters based on the reward results. Experimental validation on the MNIST dataset and industry electricity consumption dataset show that the proposed incentivized federated learning model outperforms traditional federated learning and its decentralized structure improves its robustness.