In order to achieve a fair distribution of benefits in federated learning, an indicator that quantifies the contribution of each data provider to the global model is critical. Aiming at the problems of privacy leakage, opacity and dependence on central server in existing contribution evaluation schemes, this paper proposed a transparent federated learning contribution evaluation scheme based on blockchain. Firstly, this paper proposed an improved Paillier secure aggregation algorithm, which avoided the inference of user local data in the model aggregation stage by joint decryption. Secondly, this paper proposed a method to approximate the contribution based on the gradient of user cumulative submission, which solved the problem of privacy leakage in the existing contribution evaluation scheme. In addition, it integrated the evaluation of the contribution into the consensus process of the blockchain, making the evaluation results auditable. Finally, experiments based on MNIST dataset show that the proposed method can effectively evaluate the contribution.

[2023-03-20] Accepted Paper /doc/2022.12.0821/v1

[2023-08-05] Printed Article /doc/2022.12.0821/v2