Adaptive Federated Learning and Digital Twin for Industrial Internet of Things
This work addresses efficient and reliable federated learning for Industrial IoT systems, which is incremental as it builds on existing methods with specific enhancements.
The paper tackles the problem of federated learning in Industrial IoT with digital twins by proposing a trusted aggregation method to mitigate estimation deviations and adaptive frequency adjustment using Lyapunov queues and deep reinforcement learning to improve performance under resource constraints, showing superior learning accuracy, convergence, and energy saving compared to benchmarks.
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.