Smart contract assisted secure aggregation scheme for model update in federated learning

Abstract

Federated learning (FL) is a cutting-edge machine learning architecture that effectively meets the data and model training requirements of numerous businesses, while ensuring privacy protection, data security, and adherence to legal constraints. However, recent studies have demonstrated that attackers can deduce users’ personal data from their shared model parameters, thus underscoring a substantial security risk for federated learning. Despite considerable efforts by academics to address this issue, current approaches suffer from drawbacks such as high communication and processing costs, insufficient robustness, and heavy reliance on trusted third parties. In this paper, we present the smart contract assisted secure aggregation scheme (SCSA) to overcome the aforementioned problems. First, we creatively offer a three-layer secure aggregation architecture that is particularly suitable for scenarios involving multiple sensors used in model training. Secondly, to ensure the credibility of the identities between participating entities in the model parameters aggregation process of FL, a novel certificateless authentication mechanism relying on certificateless encryption is provided. Moreover, for the sake of enhancing the security of the aggregation process in FL, we employ a combination of smart contracts and secret sharing techniques to distribute security masks to users in a decentralized and highly secure manner. It also combines with secure communication to create a double fault tolerance mechanism that significantly boosts the whole system’s robustness. The effectiveness of our approach in achieving secure aggregation is substantiated through theoretical analysis and simulated trials, while simultaneously ensuring strong security and robustness.