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Resilient Collaborative Caching for Multi-Edge Systems with Robust Federated Deep Learning

IEEE/ACM Transactions on Networking 2024年

作者： Chen, Zheyi Liang, Jie Yu, Zhengxin Cheng, Hongju Min, Geyong Li, Jie Fuzhou University College of Computer and Data Science Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350116 China Ministry of Education Engineering Research Center of Big Data Intelligence Fuzhou350002 China Lancaster University School of Computing and Communications LancasterLA1 4YW United Kingdom University of Exeter Faculty of Environment Science and Economy Department of Computer Science ExeterEX4 4QF United Kingdom Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China

As a key technique for future networks, the performance of emerging multi-edge caching is often limited by inefficient collaboration among edge nodes and improper resource configuration. Meanwhile, achieving optimal cache hit rates poses substantive challenges without effectively capturing the potential relations between discrete user features and diverse content libraries. These challenges become further sophisticated when caching schemes are exposed to adversarial attacks that seriously impair cache performance. To address these challenges, we introduce RoCoCache, a resilient collaborative caching framework that uniquely integrates robust federated deep learning with proactive caching strategies, enhancing performance under adversarial conditions. First, we design a novel partitioning mechanism for multi-dimensional cache space, enabling precise content recommendations in user classification intervals. Next, we develop a new Discrete-Categorical Variational Auto-Encoder (DC-VAE) to accurately predict content popularity by overcoming posterior collapse. Finally, we create an original training mode and proactive cache replacement strategy based on robust federated deep learning. Notably, the residual-based detection for adversarial model updates and similarity-based federated aggregation are integrated to avoid the model destruction caused by adversarial updates, which enables the proactive cache replacement adapting to optimized cache resources and thus enhances cache performance. Using the real-world testbed and datasets, extensive experiments verify that the RoCoCache achieves higher cache hit rates and efficiency than state-of-the-art methods while ensuring better robustness. Moreover, we validate the effectiveness of the components designed in RoCoCache for improving cache performance via ablation studies. © 1993-2012 IEEE.

关键词： cache space partitioning content popularity prediction Multi-edge collaborative caching proactive cache replacement robust federated deep learning

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6D Movable Antenna Enhanced Interference Mitigation for Cellular-Connected UAV Communications

arXiv

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arXiv 2024年

作者： Ren, Tianshi Zhang, Xianchao Zhu, Lipeng Ma, Wenyan Gao, Xiaozheng Zhang, Rui School of Information and Electronics Beijing Institute of Technology Beijing100081 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems Jiaxing University Jiaxing314001 China Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing University Jiaxing314001 China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China

Cellular-connected unmanned aerial vehicle (UAV) communications is an enabling technology to transmit control signaling or payload data for UAVs through cellular networks. Due to the line-of-sight (LoS) dominant air-to-ground channels, efficient interference mitigation is crucial to UAV communications, while the conventional fixed-position antenna (FPA) arrays have limited degrees of freedom (DoFs) to suppress the interference between the UAV and its non-associated co-channel base stations (BSs). To address this challenge, we propose in this letter a new approach by utilizing the six-dimensional movable antenna (6DMA) arrays to enhance the interference mitigation for the UAV. Specifically, we propose an efficient block coordinate descent (BCD) algorithm to iteratively optimize the antenna position vector (APV), array rotation vector (ARV), receive beamforming vector, and associated BS of the UAV to maximize its signal-to-interference-plus-noise ratio (SINR). Numerical results show that the proposed 6DMA enhanced cellular-connected UAV communication can significantly outperform that with the traditional FPA arrays and other benchmark schemes in terms of interference mitigation. Copyright © 2024, The Authors. All rights reserved.

关键词： Signal to noise ratio

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IMPACT: Importance-Informed Prefetching and Caching for I/O-Bound DNN Training

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IEEE Transactions on Computers 2025年

作者： Chen, Weijian He, Shuibing Zhang, Ruidong Zhang, Xuechen Chen, Ping Yang, Siling Qu, Haoyang Zhan, Xuan Zhejiang University State Key Laboratory of Blockchain and Data Security China Institute of Blockchain and Data Security Hangzhou China Zhejiang Key Laboratory of Big Data Intelligent Computing China Washington State University Vancouver School of Engineering and Computer Science VancouverWA98686 United States

Fetching large amounts of DNN training data from storage systems causes high I/O latency and GPU stalls. Importance sampling can reduce data processing on GPUs while maintaining model accuracy, but current frameworks lack a prefetching and caching layer to optimize data fetches and cache management based on sample importance. This leads to unnecessary fetches, poor cache hit ratios, and random I/Os. We present ImPACT, an importance-informed prefetching and caching system, to accelerate I/O-bound DNN training. First, we propose an importance-informed prefetching technique to reduce the prefetching of unimportant data. Then, we introduce an importance-aware caching layer, partitioned into two regions: H-cache and L-cache, which store samples of high importance and low importance respectively. Rather than using recency or frequency, we manage data items in H-cache according to their corresponding sample importance. When there is a cache miss in L-cache, we use sample substitutability and dynamic packaging to improve the cache hit ratio and reduce the number of random I/Os. Our experimental results show that ImPACT has a negligible impact on training accuracy while speeding up DNN training by up to 3.5× compared to state-of-the-art prefetching and caching systems. © 1968-2012 IEEE.

关键词： Cache memory

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Contrastive Generative Network with Recursive-Loop for 3D point cloud generalized zero-shot classification

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PATTERN RECOGNITION 2023年第1期144卷

作者： Hao, Yun Su, Yukun Lin, Guosheng Su, Hanjing Wu, Qingyao South China Univ Technol Sch Software Engn Guangzhou Peoples R China Minist Educ Key Lab Big Data & Intelligent Robot Guangzhou Peoples R China Nanyang Technol Univ Singapore Singapore Pazhou Lab Guangzhou Peoples R China Peng Cheng Lab Shenzhen Peoples R China Tencent Tencent Wechat Dept Guangzhou Peoples R China South China Univ Technol Guangzhou Higher Educ MegaCtr 382 Zhonghuan Rd East Guangzhou 510006 Peoples R China

Generalized Zero-Shot Learning (GZSL) aims to recognize objects from both seen and unseen categories by transferring semantic knowledge and merely utilizing seen class data for training. Recent feature generation methods in the 2D image domain have made great progress. However, very little is known about its usefulness in 3D point cloud zero-shot learning. This work aims to facilitate research on 3D point cloud generalized zero-shot learning. Different from previous works, we focus on synthesizing the more high-level discriminative point cloud features. To this end, we design a representation enhancement strategy to generate the features. Specifically, we propose a Contrastive Generative Network with Recursive -Loop, termed as CGRL, which can be leveraged to enlarge the inter-class distances and narrow the intra-class gaps. By applying the contrastive representations to the generative model in a recursive-loop form, it can provide the self-guidance for the generator recurrently, which can help yield more discriminative features and train a better classifier. To validate the effectiveness of the proposed method, extensive experiments are conducted on three benchmarks, including ModelNet40, McGill, and ScanObjectNN. Experimental evaluations demonstrate the superiority of our approach and it can outperform the state-of-the-arts by a large margin. Code is available at https: //***/photon-git/CGRL

关键词： 3D point cloud Generalized zero-shot Contrastive learning Recursive-loop

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Learning Semantic Context from Normal Samples for Unsupervised Anomaly Detection 35

Learning Semantic Context from Normal Samples for Unsupervis...

引用

35th AAAI Conference on Artificial Intelligence / 33rd Conference on Innovative Applications of Artificial Intelligence / 11th Symposium on Educational Advances in Artificial Intelligence

作者： Yan, Xudong Zhang, Huaidong Xu, Xuemiao Hu, Xiaowei Heng, Pheng-Ann South China Univ Technol Guangzhou Peoples R China Minist Educ Key Lab Big Data & Intelligent Robot Beijing Peoples R China Guangdong Prov Key Lab Computat Intelligence & Cy Guangzhou Peoples R China State Key Lab Subtrop Bldg Sci Guangzhou Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Key Lab Virtual Real & Human Interact Te Shenzhen Peoples R China

ISBN: (纸本)9781577358664

Unsupervised anomaly detection aims to identify data samples that have low probability density from a set of input samples, and only the normal samples are provided for model training The inference of abnormal regions on the input image requires an understanding of the surrounding semantic *** work presents a Semantic Context based Anomaly Detection Network, SCADN, for unsupervised anomaly detection by learning the semantic context from the normal samples. To achieve this, we first generate multi-scale striped masks to remove a part of regions from the normal samples, and then train a generative adversarial network to reconstruct the unseen regions. Note that the masks are designed in multiple scales and stripe directions, and various training examples are generated to obtain the rich semantic context. In testing, we obtain an error map by computing the difference between the reconstructed image and the input image for all samples, and infer the abnormal samples based on the error maps. Finally, we perform various experiments on three public benchmark datasets and a new dataset LaceAD collected by us, and show that our method clearly outperforms the current state-of-the-art methods.

关键词： Generative adversarial networks

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Towards Feature Distribution Alignment and Diversity Enhancement for data-Free Quantization

Towards Feature Distribution Alignment and Diversity Enhance...

引用

IEEE International Conference on data Mining (ICDM)

作者： Yangcheng Gao Zhao Zhang Richang Hong Haijun Zhang Jicong Fan Shuicheng Yan School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) & Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China Department of Computer Science Harbin Institute of Technology (Shenzhen) Xili University Town Shenzhen China School of Data Science The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China National University of Singapore Singapore

To obtain lower inference latency and less memory footprint of deep neural networks, model quantization has been widely employed in deep model deployment, by converting the floating points to low-precision integers. However, previous methods (such as quantization aware training and post training quantization) require original data for the fine-tuning or calibration of quantized model, which makes them inapplicable to the cases that original data are not accessed due to privacy or security. This gives birth to the data-free quantization method with synthetic data generation. While current data-free quantization methods still suffer from severe performance degradation when quantizing a model into lower bit, caused by the low inter-class separability of semantic features. To this end, we propose a new and effective data-free quantization method termed ClusterQ, which utilizes the feature distribution alignment for synthetic data generation. To obtain high inter-class separability of semantic features, we cluster and align the feature distribution statistics to imitate the distribution of real data, so that the performance degradation is alleviated. Moreover, we incorporate the diversity enhancement to solve class-wise mode collapse. We also employ the exponential moving average to update the centroid of each cluster for further feature distribution improvement. Extensive experiments based on different deep models (e.g., ResNet-18 and MobileNet-V2) over the ImageNet dataset demonstrate that our proposed ClusterQ model obtains state-of-the-art performance.

关键词： Degradation Training Quantization (signal) Semantics Neural networks Memory management data models

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Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learning

arXiv

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arXiv 2024年

作者： Zhu, Hongze Xie, Guoyang Hou, Chengbin Dai, Tao Gao, Can Wang, Jinbao Shen, Linlin National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong Department of Intelligent Manufacturing CATL Ningde China Fuzhou Fuyao Institute for Advanced Study Fuyao University of Science and Technology Fuzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

High-resolution point clouds (HRPCD) anomaly detection (AD) plays a critical role in precision machining and high-end equipment manufacturing. Despite considerable 3D-AD methods that have been proposed recently, they still cannot meet the requirements of the HRPCD-AD task. There are several challenges: i) It is difficult to directly capture HRPCD information due to large amounts of points at the sample level;ii) The advanced transformer-based methods usually obtain anisotropic features, leading to degradation of the representation;iii) The proportion of abnormal areas is very small, which makes it difficult to characterize. To address these challenges, we propose a novel group-level feature-based network, called Group3AD, which has a significantly efficient representation ability. First, we design an Intercluster Uniformity Network (IUN) to present the mapping of different groups in the feature space as several clusters, and obtain a more uniform distribution between clusters representing different parts of the point clouds in the feature space. Then, an Intracluster Alignment Network (IAN) is designed to encourage groups within the cluster to be distributed tightly in the feature space. In addition, we propose an Adaptive Group-Center Selection (AGCS) based on geometric information to improve the pixel density of potential anomalous regions during inference. The experimental results verify the effectiveness of our proposed Group3AD, which surpasses Reg3D-AD by the margin of 5% in terms of object-level AUROC on Real3DAD. We provide the code and supplementary information on our website: https://***/M-3LAB/Group3AD. Copyright © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

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data-driven Industrial robot Arm Calibration: A Machine Learning Perspective 18

Data-driven Industrial Robot Arm Calibration: A Machine Lear...

引用

18th IEEE International Conference on Networking, Sensing and Control, ICNSC 2021

作者： Li, Zhibin Li, Shuai Luo, Xin University of Chinese Academy of Sciences School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing School Chongqing China Swansea University Department of Electronics and Electrical Engineering Swansea United Kingdom University of Chinese Academy of Sciences Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing School Chongqing China

ISBN: (纸本)9781665440486

robot arms have been widely used in industry. The absolute positioning error of robots without calibration can reach several millimeters, which cannot meet the application requirements of accurate operation. Therefore, it is almost a mandatory procedure for industrial robots to take on-site calibration before being used. Generally, most researchers on robot calibration have mechanical and instrumentation background as the collection of calibration data is tedious and it is usually difficult to access to industrial robots for researchers in other fields. This research explores the calibration problem from a machine learning perspective and provides the first open-access dataset called "robotCali"in this area so that machine learning scientists can step into this field and verify their algorithms on this problem. In the meanwhile, a new calibration method based on the Levenberg-Marquardt (LM) algorithm and extended Kalman filter (EKF) algorithm is proposed, which can significantly improve the absolute positioning accuracy of the robot after calibration. Firstly, the error model of robot is established, and kinematic parameters are initially identified by LM algorithm. Then the EKF algorithm is used to further calibrate these parameters, which has been verified the effectiveness of the proposed method by experimental results. Lastly, the future research work is discussed. © 2021 IEEE.

关键词： Calibration

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Digital Mahjong System: Towards Precise Cognitive Assessment with IoT Technologies 1

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24th International Conference on Human-Computer Interaction, HCII 2022

作者： An, Ning Hu, Enze Guo, Yanrui Yang, Jiaoyun Au, Rhoda Ding, Huitong School of Computer Science and Information Engineering Hefei University of Technology Hefei China Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine Hefei University of Technology Hefei China National Smart Eldercare International S&T Cooperation Base Hefei University of Technology Hefei China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China Shenzhen Corecloud Innovation Technology Co. Ltd. Shenzhen China Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei China Department of Anatomy and Neurobiology Neurology and Framingham Heart Study Boston University School of Medicine Boston United States Department of Epidemiology Boston University School of Public Health Boston United States

ISBN: (数字)9783031179020

ISBN: (纸本)9783031179013

To the best of our knowledge, this paper is the first to apply IoT technologies to transform the popular Mahjong game into a Digital Mahjong System (DMS) for digitally performing cognitive assessments. People have started exploring digital cognitive assessment tools for better objectivity and simplification. However, most of these tools are not friendly for older adults. This paper aims to address this issue by integrating IoT technologies with Mahjong to make cognitive assessment simpler and more engaging for older adults. DMS has the following features: 1) integrating motion tracking devices into Mahjong tiles and transferring them into Digital Mahjong Tiles (DMTs), which can precisely capture their moving trajectory during the assessment, 2) using a Near Field Communication reader to configure DMT’s primary data, 3) supporting Over-The-Air Device Firmware Update that can be done remotely via Bluetooth, and 4) developing a charging base that can charge 16 DMTs simultaneously using Qi wireless charging standard. With further testing, the DMS has the potential to become a digital platform to implement and test various cognitive assessment tools integrating Mahjong elements. © 2022, Springer Nature Switzerland AG.

关键词： Internet of things

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Variable-Frequency Phase Unwrapping for High-Speed 3-D Shape Measurement

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2024年 73卷 1-1页

作者： Zeng, Jinghui Li, Yucheng Xu, Chen Li, Shutao Tan, Mingkui South China Univ Technol Shien Ming Wu Sch Intelligent Engn Guangzhou 511442 Peoples R China South China Univ Technol Sch Software Engn Guangzhou Peoples R China Hunan Univ Coll Elect & Informat Engn Changsha 410082 Peoples R China Dongguan New Generat Artificial Intelligence Techn Dongguan 523000 Peoples R China Hunan Univ Greater Bay Area Inst Innovat Guangzhou 511300 Peoples R China South China Univ Technol Sch Software Engn Minist Educ Guangzhou 510006 Peoples R China South China Univ Technol Key Lab Big Data & Intelligent Robot Minist Educ Guangzhou 510006 Peoples R China

Phase unwrapping plays a critical role in digital fringe projection (DFP) 3-D measurements. The phase unwrapping methods based on geometric constraints require no additional patterns, achieving superior efficiency and accuracy. However, our study reveals that existing methods suffer from a depth range imbalance (DRI) problem, leading to unwrapping failure and measurement error. This article presents a variable-frequency fringe pattern to address this problem. Unlike artificially defined constant-frequency fringes, we quantify the measurement depth range and theoretically determine the optimal fringe frequency according to the system parameters. Based on this, we propose a variable-frequency phase unwrapping (VFPU) method for high-speed 3-D shape measurement. The proposed method overcomes the DRI problem and maximizes the measurement accuracy and robustness. Extensive experimental results demonstrate the superiority of the proposed method in high-speed 3-D shape measurements.

关键词： Phase measurement Three-dimensional displays Frequency measurement Cameras Shape measurement Accuracy Shape 3-D shape measurement digital fringe projection (DFP) geometric constraint phase unwrapping variable frequency

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