A privacy budget adaptive optimization scheme for federated computing power Internet of things

Federated computing power Internet of things (IoT) is designed to deeply integrate computing power with IoT resources, facilitating the efficient utilization of vast and ubiquitously dispersed IoT data and heterogeneous resources through federated learning. Faced with the threats of emerging privacy attacks, e.g., model inversion attacks and gradient leakage attacks, the academic and industrial communities have widely investigated and applied differential privacy (DP) as an effective privacy protection technique. However, two severe challenges have not been taken into account in the existing DP budget settings, i.e., data heterogeneity issue of local computing power nodes and the fairness of privacy budget allocation, which lead to a significant loss in model accuracy. Therefore, an adaptive optimization scheme for privacy budget was proposed in federated computing power IoT, which was called federated learning based on Cramér-Rao lower bound differential privacy (FedCDP). In specific, to adaptively adjust privacy budgets, the privacy budget estimates for edge computing power nodes based on the Cramér-Rao lower bound theory were analyzed. Furthermore, by assessing the similarity between the local model and the aggregated model, as well as their respective privacy budget proportions, the global contribution of each node was determined, which was used to fairly, also in real time, optimize and adjust the privacy budget settings in conjunction with the estimated privacy budget. Through rigorous theoretical analysis, FedCDP achieves <italic>ε</italic>-DP for local models, and ensures the convergence of the global model. Experimental results on multiple public datasets show that the proposed scheme improves the accuracy of the global model by up to 10.19% under the premise of satisfying the same privacy protection requirements.