Responsible Image Communication-Oriented Federated Impulsive Controlled Synchronization Model for IIoT-Coupled Complex Networks

The Industrial Internet of Things coupled complex networks (IIoT-CCNs) contain a large and diverse number of nodes, whose states are inherently random and complex. In this context, federated learning can play a significant role. It allows different nodes or subsets of the IIoT-CCNs to train local models without the need to transfer all their raw data to a central server. This paper firstly establishes a more general IIoT-CCNs model, where the communication channel may not be completely opened between coupled nodes. When integrating federated learning, the nodes can collaboratively train a global model while safeguarding their own data sovereignty. Secondly, a pinning impulsive controller is designed to make IIoT-CCNs realize synchronization. Thirdly, by utilizing regroup method and step-function methods, some useful and novel synchronization stability conditions have been obtained. In the federated learning framework, these stability conditions can be adapted to ensure the convergence of the collaborative learning process. Then, a simulation is conducted to make sure the correctness of results. Finally, the application of synchronization with regard to responsible image communication is executed. The feature values of node data can be extracted through the image encryption algorithm, and then using synchronized chaotic sequences obtained from encryption to ensure the security of image information in the transmission process. Even in a federated learning-enabled IIoT-CCNs, the encrypted data and synchronized sequences can be managed in a way that respects the privacy and security requirements. It also ensures that legitimate recipients can simultaneously obtain the correct keys according to the image encryption algorithm. By utilizing the histogram and adjacent pixel correlation analysis to verify the effectiveness of the encryption scheme.