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A Dynamic Resource Efficient Asynchronous Federated Learning for Digital Twin-Empowered IoT Network

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A Dynamic Resource Efficient Asynchronous Federated Learning for Digital Twin-Empowered IoT Network

Practical Solutions for Industrial IoT Networks

Addressing Data Silos and Privacy Concerns

Digital Twin (DT) technology provides dynamic topology mapping and real-time status updates for IoT devices. However, deploying DT in industrial IoT networks can lead to data silos and privacy issues. To tackle this, a dynamic resource scheduling technique using federated learning (FL) has been developed to optimize network performance while considering energy usage and latency.

Optimizing IoT Device Selection and Transmission Power

The team has utilized the Lyapunov algorithm to break down the optimization problem into easier tasks, 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

Numerical results have demonstrated the superior performance of this technique over existing benchmark schemes. The approach achieves quicker training speeds, indicating its potential to enhance the effectiveness and efficiency of FL-based DT networks in industrial IoT scenarios.

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I believe that AI is only as powerful as the human insight guiding it.

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