The Industrial Internet of Things (IIoT) is the key technology of Industry 4.0. The combination of machine learning and IIoT has spawned a thriving smart industry. Machine learning models are trained and predicted based on raw data that contains sensitive information, and data sharing leads to information leakage. Data security and privacy protection in IIoT face serious challenges. Therefore, we propose a federated learning-based privacy-preserving data aggregation scheme (FLPDA) for IIoT. Data aggregation to protect individual user model changes in federated learning against reverse analysis attacks from industry administration centers. Each round of data aggregation uses the PBFT consensus algorithm to select an IIoT device from the aggregation area as the initialization and aggregation node. Paillier cryptosystem and secret sharing are combined to realize data fault tolerance and secure sharing. Security analysis and performance evaluation show that the scheme can effectively protect data privacy and resist various attacks. It has lower communication, computational, and storage overhead than existing schemes.

The extensive application of the Internet of Things in the industrial field has formed the industrial Internet of Things (IIoT). By analyzing and training data from the industrial Internet of Things, intelligent manufacturing can be realized. Due to privacy concerns, the industrial data of various institutions cannot be shared, which forms data islands. To address this challenge, we propose a privacy-preserving data aggregation federated learning (PPDAFL) scheme for the IIoT. In federated learning, data aggregation is adopted to protect model changes and provide data security for industrial devices. By utilizing a practical Byzantine fault tolerance (PBFT) algorithm, each round selects an IIoT device from each aggregation area as the data aggregation and initialization node, and uses data aggregation to protect the model changes of a single user while resisting reverse analysis attacks from the industrial management center. The Paillier cryptosystem and secret sharing are combined to realize data security, fault tolerance, and data sharing. A security analysis and performance evaluation show that the scheme reduces computation and communication overheads while guaranteeing data privacy, message authenticity, and integrity.