Backpack Client Selection Keeping Swarm Learning in Industrial Digital Twins for Wireless Mapping

Digital twin virtual–real mapping and precise modeling require the synchronization of large amounts of data, which leads to high communication overhead in wireless channels in industrial Internet of Things (IoT). To solve this problem, this study proposes an architecture of Digital Twin–Swarm learning (DT-SL) for industrial IoT digital twins. SL is an emerging distributed federated learning (FL) method that eliminates the need for centralized servers completely. However, it faces the problem of wireless channel congestion caused by high concurrent parameter transmission. In view of the above architecture, a novel KSL scheme based on the backpack model is used to construct the DT model. The backpack optimization problem is used to select the client with the largest contribution to participate in Keeping SL twin modeling. In addition, the experimental results evaluated the performance of the proposed method. The absolute value of the client’s updated parameter quantity decreased by 23.6% on average. The convergence rate of the aggregation model increased by 34.1%, and the model aggregation MSE value decreased to 0.03.