Distributed Learning on Mobile Devices: A New Approach to Data Mining in the Internet of Things

Zhang, Xiongtao; Zhu, Xiaomin; Bao, Weidong; Yang, Laurence T.; Wang, Ji; Yan, Hui; Chen, Huangke

doi:10.1109/jiot.2020.3030783

Cited by 4 publications

(2 citation statements)

References 24 publications

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“…To solve this, we introduce the multi-agent DRL (MA-DRL) algorithm as in [31] to solve two related sub-problems, namely, buffer-aided relay selection and IRS reflection coefficients optimization. Considering the limitations of storage, computation ability, delay and energy for a single device, we further apply distributed DRL in [32] on multiple relay nodes to reduce the training cost and improve the convergence efficiency for IRS reflection coefficients optimization. The main contributions of this paper are summarized as follows:…”

Section: Introductionmentioning

confidence: 99%

Multi-Agent Reinforcement Learning-Based Buffer-Aided Relay Selection in IRS-Assisted Secure Cooperative Networks

Huang

Chen

Wong

2021

IEEE Trans.Inform.Forensic Secur.

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This paper proposes a multi-agent deep reinforcement learning-based buffer-aided relay selection scheme for an intelligent reflecting surface (IRS)-assisted secure cooperative network in the presence of an eavesdropper. We consider a practical phase model where both phase shift and reflection amplitude are discrete variables to vary the reflection coefficients of the IRS. Furthermore, we introduce the buffer-aided relay to enhance the secrecy performance, but the use of the buffer leads to the cost of delay. Thus, we aim to maximize either the average secrecy rate with a delay constraint or the throughput with both delay and secrecy constraints, by jointly optimizing the buffer-aided relay selection and the IRS reflection coefficients. To obtain the solution of these two optimization problems, we divide each of the problems into two sub-tasks and then develop a distributed multi-agent reinforcement learning scheme for the two cooperative sub-tasks, each relay node represents an agent in the distributed learning. We apply the distributed reinforcement learning scheme to optimize the IRS reflection coefficients, and then utilize an agent on the source to learn the optimal relay selection based on the optimal IRS reflection coefficients in each iteration. Simulation results show that the proposed learning-based scheme uses an iterative approach to learn from the environment for approximating an optimal solution via the exploration of multiple agents, which outperforms the benchmark schemes.

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Section: Introductionmentioning

confidence: 99%

Multi-Agent Reinforcement Learning-Based Buffer-Aided Relay Selection in IRS-Assisted Secure Cooperative Networks

Huang

Chen

Wong

2021

IEEE Trans.Inform.Forensic Secur.

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“…On the other hand, the demand for new formats in the catering field has produced more application scenarios and requires more accurate technical requirements [ 1 ]. Under the joint action of technical support and social needs, the diet field with the characteristics of the new era has high research value in its specific application scenarios and provides a new idea for the development of the mobile Internet diet field [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Food Image Recognition and Food Safety Detection Method Based on Deep Learning

Wang

Deng

et al. 2021

Computational Intelligence and Neuroscience

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With the development of machine learning, as a branch of machine learning, deep learning has been applied in many fields such as image recognition, image segmentation, video segmentation, and so on. In recent years, deep learning has also been gradually applied to food recognition. However, in the field of food recognition, the degree of complexity is high, the situation is complex, and the accuracy and speed of recognition are worrying. This paper tries to solve the above problems and proposes a food image recognition method based on neural network. Combining Tiny-YOLO and twin network, this method proposes a two-stage learning mode of YOLO-SIMM and designs two versions of YOLO-SiamV1 and YOLO-SiamV2. Through experiments, this method has a general recognition accuracy. However, there is no need for manual marking, and it has a good development prospect in practical popularization and application. In addition, a method for foreign body detection and recognition in food is proposed. This method can effectively separate foreign body from food by threshold segmentation technology. Experimental results show that this method can effectively distinguish desiccant from foreign matter and achieve the desired effect.

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Machine-Learning-Empowered Passive Beamforming and Routing Design for Multi-RIS-Assisted Multihop Networks

Huang

Chen

Tang

et al. 2022

IEEE Internet Things J.

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Distributed Learning on Mobile Devices: A New Approach to Data Mining in the Internet of Things

Cited by 4 publications

References 24 publications

Multi-Agent Reinforcement Learning-Based Buffer-Aided Relay Selection in IRS-Assisted Secure Cooperative Networks

Multi-Agent Reinforcement Learning-Based Buffer-Aided Relay Selection in IRS-Assisted Secure Cooperative Networks

Food Image Recognition and Food Safety Detection Method Based on Deep Learning

Machine-Learning-Empowered Passive Beamforming and Routing Design for Multi-RIS-Assisted Multihop Networks

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