FedRPS: A Lightweight Federated Learning System Based on Rotated Partial Secret Sharing for Industrial Cellular Networks

As a promising distributed computing paradigm, federated learning (FL) has been widely applied to industrial manufacturing. To enhance the safety of FL system against growing privacy attacks, secret sharing techniques are introduced to realize secure model aggregation. However, the huge extra transmission in vanilla secret sharing aggravates the inherent communication bottleneck of FL system in industrial cellular networks. To reduce transmission overhead, this article proposes a lightweight FL system based on rotated partial secret sharing (FedRPS), which divides industrial terminals into several subgroups and allows them to alternately share partial local secrets. Our FedRPS ensures the global model to converge at a high rate with incomplete local parameters. Moreover, we theoretically analyze the convergence error of FedRPS, and discuss the impact of unbalanced data in subgroups on model convergence. To further improve global model performance under non-IID settings, we design a multiparty greedy grouping (MPGG) strategy to balance the training data in each subgroup. Finally, we conduct experiments on several datasets, which demonstrate our FedRPS trades for significant improvement on communication with only small loss of model accuracy. In addition, we also simulate the privacy attack scenarios to verify the better defense of FedRPS compared with previous complete secret sharing.