一种基于区块链的联邦学习贡献评价方案

为了实现联邦学习中公平的收益分配，需要有一个指标来量化每个数据提供者对联合模型的贡献。针对现有的贡献评价方案存在的隐私泄露、不透明和依赖中心服务器等问题，提出一种基于区块链的透明的联邦学习贡献评价方案。首先，提出基于改进的Paillier安全聚合算法，通过联合解密避免了在模型聚合阶段对用户本地数据的推断。其次，提出一种基于用户累计提交的梯度来近似计算其贡献的方法，解决了现有贡献评估方案存在的隐私泄露问题。此外，将贡献的评估融入到区块链的共识过程中，使其评估结果具备了可审计性。最后，基于MNIST数据集进行的实验表明，所提出的方法可以有效地评估贡献。

Blockchain-based contribution evaluation scheme for federated learning

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.