Proof-of-authority-based secure and efficient aggregation with differential privacy for federated learning in industrial IoT

Abstract

The industrial internet of things (IIoT) uses connected devices and sensors to improve efficiency in industry, but increased reliance on these systems makes them prone to faults. To ensure the reliability of IIoT systems, robust fault detection is necessary, which can be provided by artificial intelligence (AI). The current state-of-the-art approach for AI in IIoT is mainly focused on centralized learning, which is inefficient due to its high communication cost. Federated learning (FL) addresses this limitation by enabling distributed training without exposing individual information. In order to provide secure and efficient FL for the IIoT environment, several research studies have investigated the use of blockchain networks. However, most of these studies ignore the processing time metric, which is crucial for IIoT networks. In this paper, we propose a secure and efficient parameter aggregation technique that enhances trust, security, and privacy for IIoT. We use a lightweight smart contract deployed with a proof-of-authority (PoA)-based blockchain in combination with a Gaussian differential privacy mechanism. We evaluate our proposed system using a real bearing fault dataset and demonstrate that it is able to provide a secure aggregation process with an accuracy of 94.00% and a processing time of 1.54 s, which is suitable for the IIoT environment with a private blockchain network.