Practical Solutions for Industrial IoT Networks Addressing Data Silos and Privacy Concerns Digital Twin (DT) technology provides real-time updates for IoT devices, but it can lead to data silos and privacy issues in industrial IoT networks. To solve this, we've developed a dynamic resource scheduling technique using federated learning (FL) to optimize network performance while considering energy usage and latency. Optimizing IoT Device Selection and Transmission Power We've used the Lyapunov algorithm to simplify the optimization problem, deriving closed-form solutions for optimal transmit power and implementing a two-stage optimization method for IoT device scheduling. The edge server uses a multi-armed bandit (MAB) framework and an online algorithm to address device selection challenges. Enhancing FL-Based DT Networks in Industrial IoT Our approach has shown superior performance over existing benchmark schemes, achieving quicker training speeds and enhancing the effectiveness and efficiency of FL-based DT networks in industrial IoT scenarios. AI Solutions for Business Transformation Discover how AI can transform your business, identify automation opportunities, define KPIs, select AI solutions, and implement AI gradually. For AI KPI management advice and continuous insights into leveraging AI, connect with us at hello@itinai.com or stay tuned on our Telegram @itinai or Twitter @itinaicom.
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