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Efficient Byzantine-Robust Federated Learning Based on the Multi-Message Shuffle Protocol for Consumer Internet of Things

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IEEE Internet of Things Journal 2025年

作者： Jin, Chunhua Li, Lulu Wang, Jiahao Li, Jinbiao Liu, Junyi Chen, Guanhua Zhang, Hao Weng, Jian Faculty of Computer Software Engineering with Huaiyin Institute of Technology Huai’an China Huaiyin Institute of Technology Faculty of Computer Software Engineering Huai’an China Huaiyin Institute of Technology Faculty of Computer and Software Engineering Huaian China Jinan University College of Information Science and Technology China

The accelerated advancement of consumer Internet of Things (CIoT) devices has resulted in their pervasive integration into the fabric of contemporary lifestyles. As these devices are capable of collecting vast quantities of data pertaining to users, they present a significant risk to the privacy and security of individuals. Federated learning is an advanced machine learning paradigm that prioritises the preservation of user data privacy and enhances security by eliminating the need for centralised data collection. As a result, it is a promising approach to address user data privacy and security concerns. However, federated learning still faces challenges such as privacy attacks, Byzantine attacks and high communication overheads. To address these challenges, we propose an efficient Byzantine-robust federated learning method based on multi-message shuffle protocol. To achieve privacy protection and Byzantine robustness, we introduce an innovative aggregation strategy inspired by the multi-message shuffle protocol for differential privacy. To improve communication efficiency, we develop an adaptive gradient compression scheme. Theoretical analysis indicates that our multi-message shuffle protocol is capable of satisfying the requisite differential privacy conditions. Compared with the state-of-the-art methods, our proposed scheme demonstrates superior convergence capabilities on the real-world dataset when approximately 40% of participants are Byzantine, achieving accuracies of 96.78%, which illustrates its strong robustness. Upon reduction of the transmitted model parameters to 1% of the original size, the proposed scheme exhibits a maximum accuracy degradation of 0.35%. which significantly reduces the communication overhead while achieving high accuracy. In addition, the scheme also achieves an accuracy of 88.73% on the Non-iid MotionSense dataset, which is superior to other schemes. © 2014 IEEE.

关键词： Federated learning

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A Student engineering Competence Level Prediction Method Based on RNN-GRU and Dynamic Bayesian Network

A Student Engineering Competence Level Prediction Method Bas...

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2023 International Conference on Cognitive Computing and Complex Data, ICCD 2023

作者： Xu, Xiang Li, Guanxiao Xiao, Shaozhang Zhang, Zheyi Huaiyin Institute of Technology School of Computer and Software Engineering Huai'an China

ISBN: (纸本)9798350306293

To achieve effective feedback and proficiency tracking of students in contemporary educational settings, we propose a methodology that integrates the RNN-GRU model and dynamic Bayesian networks for predicting students' engineering competence level based on their online learning behaviors. The approach employed utilizes the RNN-GRU model to analyze students' progression in algorithm development skills and algorithm implementation ability, resulting in the generation of a historical record of their learning features. These features are then employed to train a dynamic Bayesian network model, facilitating the prediction of students' engineering competence level. After the amount of data reaches a certain amount, it can compare the previous models such as RNN and LSTM to obtain higher accuracy. © 2023 IEEE.

关键词： Students

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Multi-angle face expression recognition based on integration of lightweight deep network and key point feature positioning

Multi-angle face expression recognition based on integration...

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International Conference on Emerging Electronic and Automation technology 2022, ICEEAT 2022

作者： Gao, Han Mokhtarzadeh, Amir Ali Li, Shaofan Fei, Hongyan Geng, Junzuo Wang, Deye College of Computer and Software Engineering Huaiyin Institute of Technology Huaian223001 China

In the current advancement of science and technology, robot and machine vision technology have significant application value. Due to their mimetic structure, quadruped robots are better equipped to adapt to the requirements of walking over complex terrain. This paper introduce lightweight deep network and combining key point feature positioning for multi-angle face expression recognition. Using robot dog to recognize facial expressions will be affected by distance and angle. To solve this problem, this paper proposes a method for facial expression recognition at different distances and angles,which solved the larger distance and deflection angle of face expression recognition accuracy and real-time issues. The dataset used in this study was collected by adjusting the camera mounted in an AI robotic dog in a laboratory scenario. The result shows a common and fast and intuitive visual representation of human beings to convey emotions or mental states, facial expression is an effective way for robots to obtain information and interact with people through visual images. © Published under licence by IOP Publishing Ltd.

关键词： Face recognition

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DM-Net for Colorectal Polyp Detection Using Dual Feature and Multi-Layer Aggregation 12

DM-Net for Colorectal Polyp Detection Using Dual Feature and...

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12th International Conference on Advanced Cloud and Big Data, CBD 2024

作者： Wan, Jingjing Wang, Ling Zhu, Wenjie Chen, Bolun The Second People's Hospital of Huai'an Department of Gastroenterology Huaian China Huaiyin Institute of Technology Faculty of Computer and Software Engineering Huaian China

ISBN: (纸本)9798331511074

Early detection of colorectal polyps is crucial for preventing colorectal cancer. Although endoscopy is the current standard diagnostic method, it still faces challenges in terms of accuracy, efficiency, and patient comfort. To address these issues, this paper proposes a colorectal polyp detection model named DM-Net. This model utilizes the Dual Feature Integration Block (DFIB) to integrate channel and spatial features, enhancing feature extraction efficiency. Additionally, the model incorporates the Multi-Layer Path Aggregation Network (MLPAN) to handle the multi-scale variations of polyps. Experimental results demon-strate that DM-Net significantly outperforms existing methods in terms of accuracy and efficiency, offering high clinical applica-bility. © 2024 IEEE.

关键词： Endoscopy

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Pose-aware Multi-position Feature Network for Driver Distraction Recognition

Pose-aware Multi-position Feature Network for Driver Distrac...

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International Conference on Emerging Electronic and Automation technology 2022, ICEEAT 2022

作者： Li, Shaofan Mokhtarzadeh, Amir Ali Gao, Han Zhang, Yingying College of Computer and Software Engineering Huaiyin Institute of Technology Huaian223001 China

This paper introduce pose-aware multi-position feature network for driver distraction recognition, taking into account the high association between the visual and geometric features of neighboring human key points. This method first uses the object detector to detect the driver's body, and then uses the pose estimation to detect the key points of the human body. Finally, the key points of the human body are deconstructed to obtain key visual features and spatial *** order to create the visual representation that corresponds to each key point, all the chosen visual representations are sent into a convolutional neural network. The geometric locations of key points on the upper body are used to build the spatial reasoning module, which is then transmitted to the neural network to get spatial features. The visual appearance features are fused with geometric features to predict corresponding driver action through linear *** experimental result shows that our methods achieve comparative performance on our own HY Large Vehicle Driver Dataset and the public AUC Driver Distracted Dataset. © Published under licence by IOP Publishing Ltd.

关键词： Object detection

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Cleaning Uncertain Time Series Based on Random Walk Sampling 4

Cleaning Uncertain Time Series Based on Random Walk Sampling

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4th International Conference on High Performance Big Data and Intelligent Systems, HDIS 2022

作者： Zhou, Delin Sun, Jizhou Jiang, Bo Hudoyberdi, Toshboev Lin, Jianchu Huaiyin Institute of Technology Faculty of Computer and Software Engineering Huai'an China

ISBN: (数字)9781665491440

ISBN: (纸本)9781665491440

In the era of big data, there are many different data types, and time series data makes up a large portion of observational data. However, when using sensors to collect time series data, the observation values are not very accurate due to the quality of the sensors and the observation errors. So how to estimate true data from multiple uncertain sensors' observations has becoming a hot research topic in IoT and big data fields. Several data cleaning methods for time series have been proposed, but many of them have strong constraints. Intuitively, if two observations are close, their contributions to true data estimation should be similar. According to this idea, in this paper we propose a novel data cleaning and truth discovery method named RWTD for time series based on random walk sampling. Distances between observations are calculated to build a state graph, and random walk sampling is used to estimate the weights of data sources. True data is estimated as the weighted sum of observations. Experimental results demonstrate the effectiveness of our method. © 2022 IEEE.

关键词： Time series

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YOLO-BTM: A Novel Shuttlecock Detection Method for Embedded Badminton Robots

YOLO-BTM: A Novel Shuttlecock Detection Method for Embedded ...

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2022 International Conference on Automation, Robotics and computer engineering, ICARCE 2022

作者： Zhang, Yimin Chen, Chuxuan Hu, Ronglin Huaiyin Institute of Technology Faculty of Computer & Software Engineering Huai'an China

ISBN: (纸本)9781665475488

Employing robots in badminton training contributes to a more accurate analysis of an athlete's movements and helps avoid injuries. Shuttlecock detection during the flying stage is a critical component of the badminton robot design. However, previous shuttlecock localization methods were unable to detect shuttlecock quickly and accurately in embedded device-based badminton robots, given scale variations, few extractable features, occlusion, and device limitation. In this paper, a deep learning-based shuttlecock localization method is proposed. First, an indoor shuttlecock dataset including 9548 shuttlecock images of various angles and scenes was constructed. Then a shuttlecock detection method YOLO-BTM is proposed, which is based on YOLOv4-Tiny. We proposed a new convolution block to replace the cross-stage partially block in the backbone, to improve the detection speed. To improve the network's ability to detect small objects, the efficient channel attention block is introduced in feature fusion. Finally, a comparative experiment on the accuracy of the method and the detection speed was conducted. The results show that the proposed YOLO-BTM has better performance in detection speed and accuracy compared to the existing state-of-the-art object detection methods on our own shuttlecock dataset. Our method enables real-time, accurate localization of shuttlecock and has the potential to be used in other embedded device based sports robots. © 2022 IEEE.

关键词： Object detection

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Steel surface defect detection based on Improved YOLOX Algorithm

Steel surface defect detection based on Improved YOLOX Algor...

引用

International Conference on Emerging Electronic and Automation technology 2022, ICEEAT 2022

作者： Li, Guohao Mokhtarzadeh, Amir Ali School of Computer and Software Engineering Huaiyin Institute of Technology Jiangsu Huaian223003 China

As one of the very common industrial products, strip steel plays a very important role in various fields. Steel production has received extensive attention. In industrial production, scratches are unavoidable due to numerous forces beyond human control. Cracks and other flaws appear, and their presence has a direct impact on the product's quality. Therefore, it is of great significance to develop efficient and accurate strip surface defect detection. In this study, the most widely used metal material strip steel is selected as the research object, and its surface defect detection problem is studied accordingly. The activation function and loss function are improved on the basis of the YOLOX model. Resolve the problem of overexposure and content distortion of the image caused by the high reflecting characteristics of the strip, which degrades the image quality, and the embedding of the sub-attention mechanism to enhance the regional characteristics of hidden small targets. The experimental results show that the mAP of the improved model for strip surfaces defect detection is 80.7%, which is 4.1% higher than that of the original YOLOX algorithm, and the improved model can reduce the false detection and missed detection rate of defects. © Published under licence by IOP Publishing Ltd.

关键词： Strip metal

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Novel cartographer using an OAK-D smart camera for indoor robots location and navigation

Novel cartographer using an OAK-D smart camera for indoor ro...

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International Conference on Emerging Electronic and Automation technology 2022, ICEEAT 2022

作者： Han, Yunpeng Ali Mokhtarzadeh, Amir Xiao, Shaozhang School of Computer and Software Engineering Huaiyin Institute of Technology Jiangsu Huai'an223003 China

In recent years, service robots have been widely used in people's daily life, and with the development of more and more intelligence, people put forward higher requirements for autonomous positioning and navigation functions of robots. Like outdoor navigation, indoor navigation also needs the support of navigation data. Although the indoor positioning and navigation scheme based on cameras, lidars and other sensors is gradually developing, due to the complexity of the indoor structure, manual production of indoor navigation data is time-consuming and laborious, and the efficiency is relatively low. In order to solve the problem of low productivity and improve the accuracy of robot automatic navigation, we added a new type of intelligent camera, called OpenCV AI kit or OAK-D, and proposed a method to automatically build data files that can be used for indoor navigation and location services using indoor 3D point cloud data. This intelligent camera performs neural reasoning on chips that do not use GPUs. It can also use stereo drills for depth estimation, and use 4K color camera images as input to run the neural network model. Python API can be called to realize real-time detection of indoor doors, windows and other static objects. The target detection technology uses an artificial intelligence camera, and the robot can well identify and accurately mark on the indoor map. In this paper, a high-performance indoor robot navigation system is developed, and multisensor fusion technology is designed. Environmental information is collected through artificial intelligent camera (OAK-D), laser lidar, and data fusion is carried out. In the experiment part of this paper,The static fusion map module is created based on the laser sensor information and the sensor information of the depth camera, the hierarchical dynamic cost map module is created in the real-time navigation, and the global positioning of the robot is realized by combining the word bag model and the laser point cl

关键词： Cameras

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Lightweight medical image segmentation network based on ghost convolution and attention mechanism 2

Lightweight medical image segmentation network based on ghos...

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2nd International Symposium on computer Applications and Information Systems, ISCAIS 2023

作者： Wang, Haoran Yu, Kun Li, Qiangqiang Guan, Qianjun Gao, Shihong College of Computer and Software Engineering Huaiyin Institute of Technology Jiangsu Huai’an223003 China

ISBN: (纸本)9781510666665

Ghostnet is a lightweight image classification network proposed by Kai Han and other scholars in 2019 which was applied in terms of object detection. Ghostnet has the advantages of few parameters and high accuracy. In this paper, through the idea of transfer learning, ghost net is used as the network of feature extraction and CBAM attention mechanism is added. CBAM is not only a lightweight attention mechanism, but also a good combination of space and channels. CBAM eliminate irrelevant noise intelligences and concern to key intelligences. At the same time, the ghost convolution block is improved, and point-by point convolution is added to the ghost convolution block to obtain more image feature information. Add the squeeze and enumeration module to improve the convolution receptive field, increase the ability of the network to extract multi-scale spatial information, and introduce the residual idea to tackle the problem of information loss and gradient descent disappearance. GAUnet, designed according to the above idea, achieves better intersection and merger ratio than Unet, Unet++, DeepLabV3+and other neural networks on the multi- organ segmented chaos dataset with fewer parameters. © 2023 SPIE.

关键词： Convolution

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