Secure Rational Delegation Federated Learning

Federated learning (FL) allows multiple distributed clients with local datasets to train a global model collaboratively. Due to the potential privacy risk of the training process, differential privacy (DP) is introduced into FL to protect clients’ sensitive information by perturbing the model updates. However, the probability density function of the Laplace mechanism has a long-tail effect, which may generate large noise to induce the model to deviate from the normal result. Moreover, as the cloud is not fully trusted, there is no guarantee that the server follows the aggregation protocol correctly. To address these issues, in this paper, we propose a secure rational delegation FL scheme, namely SRDFL, and analyze its protection and convergence performance. Specifically, we first utilize the zero-determinant strategy to construct a FL rational model. It delegates tasks to multiple servers and encourages them to perform correct aggregation. Then, we design a bounded DP protection mechanism to achieve a fixed universe of perturbation outputs in a threshold-constrained manner. Finally, based on Shamir’s secret sharing, we propose a trusted verification algorithm of DP to validate servers for correct aggregation. Detailed theoretical analysis and extensive performance evaluations demonstrate that our proposed scheme is effective. Compared to existing works, SRDFL is able to improve 2.72% - 47.92% model accuracy.