A Survey on Collaboration Computing for Industrial Internet of Things: Digital Twin, Federated Learning, and Swarm Learning

The Industrial Internet of Things(IIoT) deepens the collaborative computing between different devices and control layers. IIoT, as a dynamic, time-varying and complex environment, digital twin(DT) drives the real-time dynamic mapping between physical devices and twins to achieve global control of industrial production lines. This paper reviews the relevant research on the application and deployment of DT in the IIoT, which provides technical support for cross-production line data sharing and accurate decision-making. In particular, the related work of the multi-layer collaborative computing architecture built by federated learning(FL) in DT-IIoT is reviewed. We systematically explore the key technical breakthroughs of DT and FL in the IIoT, such as data security collaboration, dynamic resource scheduling, computing efficiency improvement, and cross-domain collaborative computing, from four technologies: edge computing, blockchain, deep reinforcement learning, and personalized learning. On this basis, we have explored and experimented in detail with fully decentralized SL in IIoT. SL-knowledge distillation(KD), it can solve the challenge of reducing the reliability of global model decisions in SL due to multi-source heterogeneous data in complex industrial scenarios. Experimental results show that SLKD outperforms other baselines by an average of 0.024 and 0.060 in recall and accuracy. In addition, we discuss the current challenges and future research directions.