Long-Term Privacy-Preserving Incentive Scheme Design for Federated Learning

Differential-privacy federated learning (DP-FL) has emerged as a promising approach to mitigate the inherent risks associated with traditional FL architectures, which are susceptible to inferential attacks due to the continuous sharing and updating of model parameters. However, existing DP-FL frameworks typically assume that the perturbations introduced by clients remain constant throughout the FL process, overlooking the dynamic influence of these perturbations on model performance across different communication rounds. In this paper, we present a long-term privacy-preserving FL framework designed to address issues of optimal incentive design, considering the dynamic influence of perturbations on model performance. Specifically, we first analyze the effect of local perturbations on the model’s convergence performance during various communication rounds, elucidating the balance between learning performance and privacy loss. Then, to harmonize learning performance with privacy loss, we develop a long-term privacy-preserving incentive scheme, where the interactions between clients and the FL server throughout the FL process are modeled as a multi-stage privacy-preserving game. Furthermore, we utilize contract theory to derive the equilibrium of this game. Finally, simulations show that our scheme can incentivize clients to contribute high-quality models, thereby enhancing the accuracy of the global model, as compared to benchmarks.