Secure sharing of industrial IoT data based on distributed trust management and trusted execution environments: a federated learning approach

Industrial Internet of Things (I-IoT) has become an emerging driver to operate industrial systems and a primary empowerer to future industries. With the advanced technologies such as artificial intelligence (AI) and machine learning widely used in IoT, the Industrial IoT is also witnessing changes driven by new technologies. Generally, AI technologies require centralized data collection and processing to learn from the data to obtain viable models for application. In industrial IoT, data security and privacy problems associated with reliable and interconnected end devices are being faced and reliable solutions are urgently needed. A trusted execution environment in IoT devices is gradually becoming a feasible approach, and a distributed solution is a natural choice for artificial intelligence technologies in I-IoT. Moreover, Federated Learning as a distributed machine learning paradigm with privacy-preserving properties can be used in I-IoT. This paper introduces a feasible secure data circulation and sharing scheme for I-IoT devices in a trusted implementation platform by employing federated learning. The suggested framework has proved to be efficient, reliable, and accurate.