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The rapid development of emerging technologies, such as edge intelligence and digital twins, have added momentum towards the development of the Industrial Internet of Things (IIoT). However, the massive amount of data generated by the IIoT, coupled with heterogeneous computation capacity across IIoT devices, and users' data privacy concerns, have posed challenges towards achieving industrial edge intelligence (IEI). To achieve IEI, in this paper, we propose a semi-federated learning framework where a portion of the data with higher privacy is kept locally and a portion of the less private data can be potentially uploaded to the edge server. In addition, we leverage digital twins to overcome the problem of computation capacity heterogeneity of IIoT devices through the mapping of physical entities. We formulate a synchronization latency minimization problem which jointly optimizes edge association and the …