Digital twin (DT) technology is becoming increasingly popular as a method of providing Internet of Things (IoT) devices with dynamic topology mapping and real-time status updates. However, there are difficulties in implementing DT in industrial IoT networks, especially when significant, dispersed data support is required. This frequently results in the creation of data silos, where data is located within certain systems or devices, making it difficult to collect and examine data from across the network. Furthermore, because sensitive information can be subject to abuse or disclosure, the collection and use of dispersed data creates serious privacy concerns.
To address these issues, a team of researchers has created a dynamic resource scheduling technique especially for a DT-enabled asynchronous and lightweight IoT network using federated learning (FL). The goal of this method is to minimize a multi-objective function that takes into account latency and power usage to maximize network throughput. By doing this, the team has ensured that transmission power is managed and IoT devices are chosen in a way that meets the performance requirements of the FL model.
The strategy is based on the mathematically proven Lyapunov algorithm, which guarantees the stability of the system. Using this technique, the difficult optimization problem has been divided into several simpler single-slot optimization problems. Then, to arrive at the best plans for scheduling IoT devices and controlling transmission power, the team has created a two-stage optimization method.
The team first built closed-form solutions for the optimal transmission power of the IoT device. This step ensures that each device transmits data effectively and with the lowest possible energy, while maintaining the required communication quality. The IoT device selection problem has been addressed in the second stage, which is aggravated by the unknown state information of the transmission power and computational frequency.
The edge server uses a multi-armed bandit (MAB) framework, a decision-making model that helps in selecting the optimal option from a number of fuzzy options to handle this. The device selection problem has been solved by using an efficient online technique called customer utility-based confidence upper bound (CU-UCB).
Numerical results have verified the utility of this technique, demonstrating its superior performance over current baseline schemes. Simulations performed on datasets such as Fashion-MNIST and CIFAR-10 have shown that this approach achieves faster training speeds in the same amount of time, indicating its potential to improve the effectiveness and efficiency of FL-based DT networks in industrial IoT scenarios.
The team has summarized its main contributions as follows.
- A dynamic resource scheduling technique is designed for asynchronous federated learning in a lightweight Digital Twin (DT)-driven IoT network, addressing the problems of data silos and privacy concerns in industrial IoT.
- The goal of the algorithm is to minimize a multi-objective function to improve the overall performance of asynchronous FL. This function optimizes IoT device selection and transmission power regulation, while respecting the performance limits of the FL model by considering both power usage and latency.
- The complicated optimization problem has been broken down into simpler single-slot optimization tasks in the paper using the Lyapunov approach. Hard testing and optimization have been used to derive closed-form solutions for optimal transmission power at the IoT device side.
- A multi-armed bandit (MAB) framework has been used to represent the IoT device selection problem at the edge server side, where some state information is unknown. This problem has been addressed using an efficient online algorithm, the client utility-based upper confidence bound.
- The study has further shown that the method achieves sublinear regret in the communication rounds by deriving the theoretical optimality gap. Within the same training duration, the Fashion-MNIST and CIFAR-10 datasets have shown that the proposed CU-UCB method achieves faster training speeds than the baseline approaches, as validated by the numerical findings.
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Tanya Malhotra is a final year student of the University of Petroleum and Energy Studies, Dehradun, pursuing BTech in Computer Engineering with specialization in artificial intelligence and Machine Learning.
She is a data science enthusiast with good analytical and critical thinking, along with a keen interest in acquiring new skills, leading groups, and managing work in an organized manner.
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