Lightweight Tensor-Enabled GRU for Trustworthy and Communication Efficient Federated Learning in Industrial IoT
Ruonan Zhao, Laurence Tianruo Yang, Debin Liu, Wanli Lu, Xiangli Yang · IEEE Transactions on Industrial Informatics · 2024
Deep learning provides an intelligent analytical approach for Big Data analysis and feature extraction in Industrial Internet of Things (IIoT). However, due to concerns about data security and privacy disclosure, conventional data-centralized deep learning often faces difficulties about data famine and data islands. Federated learning (FL) as a novel privacy-preserving deep learning paradigm breaks the data islands among different smart factories by sharing their model parameters instead of raw data, which essentially solves the data famine problem for training a high-quality deep learning model. Nevertheless, exchanging numerous model parameters not only generates considerable communication overhead but also poses the risk of privacy information disclosure hidden in model parameters due to inference attacks launched by external attackers orhonest-but-curiousservers. The purpose of this article is to build a high-quality trustworthy FL architecture dubbed TrustFedGRU for IIoT while alleviating the communication overhead. First, a multikey decryption assisted privacy-preserving homomorphic encryption scheme is proposed in FL to meet the distinct privacy-preserving requirements of different data owners without impairing model performance. Furthermore, tensor decomposition is leveraged to convert the weight tensor of the gated recurrent unit (GRU) into a low-rank approximation, so as to reduce the communication bandwidth overhead and decrease the storage requirements. Meanwhile, a novel dynamic update-based FL approach is investigated to improve the model performance. The experimental results show that the proposed TrustFedGRU greatly reduces the communication overhead while guaranteeing the model performance and security.