Lightweight privacy-preserving predictive maintenance in 6G enabled IIoT

While the 5G is being rolled out in different industrial sectors, the 6G is expected to implement data-driven ubiquitous machine learning for industrial information integration environment. The machine learning algorithms show great potential in predictive maintenance where data is collected over time. However, the privacy issues become increasingly challenging since the huge amount of data are exchanged in the machine learning based solutions. On the other hand, it is very important to lightweightise the machine learning by leveraging effective machine learning models without restriction of privacy concerns. The homomorphic encryption techniques make it possible to perform machine learning model over encrypted data to address above challenges. This work introduced a lightweight privacy-preserving predictive maintenance technique based on machine learning in 6G-IIoT scenarios, which can help to quantify the risk of failures for industrial assets or systems in any moment. Binary neural networks (BNNs) were introduced to train predictive maintenance model, that can be implemented with homomorphic encryption circuits to guarantee the privacy of all participants. Experimental results demonstrate the effectiveness of the proposed solution.