  Industrial Internet of Things (IoT) enables distributed intelligent services
varying with the dynamic and realtime industrial devices to achieve Industry
4.0 benefits. In this paper, we consider a new architecture of digital twin
empowered Industrial IoT where digital twins capture the characteristics of
industrial devices to assist federated learning. Noticing that digital twins
may bring estimation deviations from the actual value of device state, a
trusted based aggregation is proposed in federated learning to alleviate the
effects of such deviation. We adaptively adjust the aggregation frequency of
federated learning based on Lyapunov dynamic deficit queue and deep
reinforcement learning, to improve the learning performance under the resource
constraints. To further adapt to the heterogeneity of Industrial IoT, a
clustering-based asynchronous federated learning framework is proposed.
Numerical results show that the proposed framework is superior to the benchmark
in terms of learning accuracy, convergence, and energy saving.
