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arXiv 2023年

作者： Liu, Jiahao Wu, Jiang Chen, Jinyu Hu, Miao Zhou, Yipeng Wu, Di School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China School of Computing Faculty of Science and Engineering Macquarie University Sydney Australia

Different from conventional federated learning, personalized federated learning (PFL) is able to train a customized model for each individual client according to its unique requirement. The mainstream approach is to adopt a kind of weighted aggregation method to generate personalized models, in which weights are determined by the loss value or model parameters among different clients. However, such kinds of methods require clients to download others’ models. It not only sheer increases communication traffic but also potentially infringes data privacy. In this paper, we propose a new PFL algorithm called FedDWA (Federated Learning with Dynamic Weight Adjustment) to address the above problem, which leverages the parameter server (PS) to compute personalized aggregation weights based on collected models from clients. In this way, FedDWA can capture similarities between clients with much less communication overhead. More specifically, we formulate the PFL problem as an optimization problem by minimizing the distance between personalized models and guidance models, so as to customize aggregation weights for each client. Guidance models are obtained by the local one-step ahead adaptation on individual clients. Finally, we conduct extensive experiments using five real datasets and the results demonstrate that FedDWA can significantly reduce the communication traffic and achieve much higher model accuracy than the state-of-the-art approaches. Copyright © 2023, The Authors. All rights reserved.

关键词： data privacy

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Distributed Computing and Artificial Intelligence 1

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丛书名： Advances in Intelligent and Soft Computing

1000年

作者： Sigeru Omatu Juan F. De Paz Santana Sara Rodríguez González Jose M. Molina Ana M. Bernardos Juan M. Corchado Rodríguez

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BARA: Efficient Incentive Mechanism with Online Reward Budget Allocation in Cross-Silo Federated Learning

arXiv

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arXiv 2023年

作者： Yang, Yunchao Zhou, Yipeng Hu, Miao Wu, Di Sheng, Quan Z. School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China School of Computing Faculty of Science and Engineering Macquarie University Sydney Australia

Federated learning (FL) is a prospective distributed machine learning framework that can preserve data privacy. In particular, cross-silo FL can complete model training by making isolated data islands of different organizations collaborate with a parameter server (PS) via exchanging model parameters for multiple communication rounds. In cross-silo FL, an incentive mechanism is indispensable for motivating data owners to contribute their models to FL training. However, how to allocate the reward budget among different rounds is an essential but complicated problem largely overlooked by existing works. The challenge of this problem lies in the opaque feedback between reward budget allocation and model utility improvement of FL, making the optimal reward budget allocation complicated. To address this problem, we design an online reward budget allocation algorithm using Bayesian optimization named BARA (Budget Allocation for Reverse Auction). Specifically, BARA can model the complicated relationship between reward budget allocation and final model accuracy in FL based on historical training records so that the reward budget allocated to each communication round is dynamically optimized so as to maximize the final model utility. We further incorporate the BARA algorithm into reverse auction-based incentive mechanisms to illustrate its effectiveness. Extensive experiments are conducted on real datasets to demonstrate that BARA significantly outperforms competitive baselines by improving model utility with the same amount of reward budget. Copyright © 2023, The Authors. All rights reserved.

关键词： data privacy

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data exchange platform for digital economy applications 6

Data exchange platform for digital economy applications

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6th International Conference Information Technology and Nanotechnology. Session data science, ITNT-DS 2020

作者： Surnin, Oleg Ivaschenko, Anton Sitnikov, Pavel Stolbova, Anastasia Khorina, Anastasia Ilyasova, Nataly Ipsi Sec Open Code Data Analysis Department Samara Russia Samara State Technical University Computer Science Department Samara Russia Faculty of Infocommunication Itmo University Saint-Petersburg Russia Samara National Research University Information Systems and Technologies Department Samara Russia Faculty of Infocommunication Itmo University Saint-Petersburg Russia Branch of Fsrc Crystallography Image Processing Systems Institute of Ras and Photonics" RAS Samara Russia

This paper describes an experience of the data exchange software platform practical use supporting the modern trends of Digital Economy. The platform was initially designed for the suppliers and customers of data sources providing the up-to-date technologies of big data processing as an online service. The platform is also open for software developers to upload new algorithms and technologies in order to help them to find new areas of application. There is presented architecture and its software implementation for an intermediary online platform capable of collecting, processing and analysis of various datasets. Modern companies being the members of digital economy can use this platform to process their data and produce business analytics. They can become both suppliers and providers of data, as well as develop and upload new customized algorithms. First results were achieved in the area of retail and social media analysis. Copyright © 2020 for this paper by its authors.

关键词： Electronic data interchange

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Supervised Anomaly Detection for Production Line Images using data Augmentation and Convolutional Neural Network

Supervised Anomaly Detection for Production Line Images usin...

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Saara Asif Muhammad Uzair Akmal Leonid Koval Simon Knollmeyer Selvine G. Mathias Daniel Grossmann AI Motion Technische Hochschule Ingolstadt Ingolstadt Germany AI Motion Bavaria Technische Hochschule Ingolstadt Ingolstadt Germany Faculty of Computer Science and Data Processing Technische Hochschule Ingolstadt Ingolstadt Germany

ISBN: (数字)9798350361230

ISBN: (纸本)9798350361247

In the manufacturing industry, automated optical inspection aims to improve the detection and classification of anomalies by utilizing artificial intelligence and computer vision techniques to enhance quality control processes and minimize production defects. However, this automated system faces significant challenges, particularly regarding the detection of anomalies due to predominance of normal instances over defected ones. Addressing this imbalance is crucial for effective real-time anomaly detection particularly in images captured by Airbag Sensors among other automotive parts. Earlier contributions in domain-specific fields commonly relied on traditional computer vision methods, while recent systems are increasingly using deep learning techniques. Utilizing various data augmentation techniques ensures a more balanced representation of anomalies in the dataset, thereby enhancing the accuracy of the detection process. Moreover, it also enhances the robustness and generalization of the anomaly detection model by exposing it to a more diverse range of instances during training. Such work has not been carried out to augment Airbag Sensor images for analysis through a deep learner. Accordingly, this paper introduces a framework that employs data augmentation techniques for Convolutional Neural Networks (CNNs). The proposed system, based on data augmentation and CNN, significantly improves the performance for anomaly detection in Airbag Sensor images with a classification accuracy on the unaugmented dataset being 53 % which improves to 90% with augmentation.

关键词： Image sensors Training computer vision Accuracy Production data augmentation Sensor systems Robustness Convolutional neural networks Anomaly detection

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Fast Image Reconstruction in Ultrasound Transmission Tomography by U-net

Fast Image Reconstruction in Ultrasound Transmission Tomogra...

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IEEE Symposium on Nuclear science (NSS/MIC)

作者： Hongjian Wang Xueze Qian Hartmut Gemmeke Torsten Hopp Nicole V. Ruiter Jürgen Hesser School of Computer Science and Technology Donghua University Shanghai China Institute for Data Processing and Electronics Karlsruhe Institute of Technology (KIT) Karlsruhe Germany Medical Faculty Mannheim Heidelberg University Mannheim Germany

Conventional iterative methods for image reconstruction in ultrasound transmission tomography need to perform many iterations where at each iteration one has to compute the complex forward model of ultrasound wave propagation, and hence they are time-consuming. We use a U-net neural network to accelerate the reconstruction, by training the network to map from an initial reconstruction obtained via a few iterations of L-BFGS method to the target ground truth image. Since the computation of a forward pass of the neural network is very fast, we can expect a significant acceleration using the trained network for image reconstruction. Experiments show that our trained network can replace 40 L-BFGS iterations to generate equivalent reconstructions with slightly better quantitative quality in terms of normalized root mean square error and better visual quality due to the network's denoising effect. It can achieve up to 283× speedup compared with L-BFGS method for reconstructing small-size sound speed images with 80×80 pixels. This implies that we can expect even greater acceleration effects when applying such approach to reconstruct large-size 3D images.

关键词： Training Visualization Ultrasonic imaging Three-dimensional displays Propagation Neural networks Noise reduction

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Decomposed Neural Architecture Search for image denoising

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Applied Soft Computing 2022年 124卷

作者： Li, Di Bai, Yunpeng Bai, Zongwen Li, Ying Shang, Changjing Shen, Qiang School of Computer Science National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Provincial Key Laboratory of Speech & Image Information Processing Northwestern Polytechnical University Xi'an710129 China Department of Computer Science Faculty of Business and Physical Sciences Aberystwyth University AberystwythSY233DB United Kingdom Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data School of Physics and Electronic Information Yan'an University Yan'an716000 China

In practical applications of deep learning, as the demand for the modeling capability increases, the network size may need to be massively enlarged in response. This may form a significant challenge in practice, especially when facing the dilemma of limited computational resources, making model compression indispensable. It can be time-consuming and interminable to obtain an appropriate network architecture through manual compression. In this paper, we propose an automated method for searching decomposed network architectures, named DNAS (standing for Decomposed Neural Architecture Search). It integrates both tasks of neural architecture search and tensor decomposition based model compression within a unified framework. The method is able to efficiently find a compact network with high performance for image denoising, with respect to memory and runtime. Particularly, using one single V100 GPU, it only takes about 1.5 h to obtain a denoising network on the BSD500 dataset. Experimental results demonstrate that compared with models developed using existing methods, DNAS consumes significantly less inference time and employs much fewer trainable parameters, outperforming existing approaches on both synthetic and real-world denoising datasets. © 2022 Elsevier B.V.

关键词： Network architecture

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VIQI: A new approach for visual interpretation of deep web query interfaces

VIQI: A new approach for visual interpretation of deep web q...

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International Conference on Information Technology and e-Services (ICITeS)

作者： Radhouane Boughammoura Lobna Hlaoua Mohamed Nazih Omri Department of Sciences of Data Processing Faculty of Sciences of Monastir Research Unit MARS Monastir Tunisia Department of Electronic and Computer Science High School of Sciences and Technology of H.Sousse Research Unit MARS Tunisia

Deep Web databases contain more than 90% of pertinent information of the Web. Despite their importance, users don't profit of this treasury. Many deep web services are offering competitive services in term of prices, quality of service, and facilities. As the number of services is growing rapidly, users have difficulty to ask many web services in the same time. In this paper, we imagine a system where users have the possibility to formulate one query using one query interface and then the system translates query to the rest of query interfaces. However, interfaces are created by designers in order to be interpreted visually by users, machines can not interpret query from a given interface. We propose a new approach which emulates capacity of interpretation of users and extracts query from deep web query interfaces. Our approach has proved good performances on two standard datasets.

关键词： Web services Visualization Web pages Semantics Feature extraction Standards Noise

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Attributed Multi-order Graph Convolutional Network for Heterogeneous Graphs

arXiv

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arXiv 2023年

作者： Chen, Zhaoliang Wu, Zhihao Zhong, Luying Plant, Claudia Wang, Shiping Guo, Wenzhong College of Computer and Data Science Fuzhou University Fuzhou350116 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China Faculty of Computer Science University of Vienna Vienna1090 Austria ds:UniVie Vienna1090 Austria

Heterogeneous graph neural networks aim to discover discriminative node embeddings and relations from multi-relational networks. One challenge of heterogeneous graph learning is the design of learnable meta-paths, which significantly influences the quality of learned embeddings. Thus, in this paper, we propose an Attributed Multi-Order Graph Convolutional Network (AMOGCN), which automatically studies meta-paths containing multi-hop neighbors from an adaptive aggregation of multi-order adjacency matrices. The proposed model first builds different orders of adjacency matrices from manually designed node connections. After that, an intact multi-order adjacency matrix is attached from the automatic fusion of various orders of adjacency matrices. This process is supervised by the node semantic information, which is extracted from the node homophily evaluated by attributes. Eventually, we utilize a one-layer simplifying graph convolutional network with the learned multi-order adjacency matrix, which is equivalent to the cross-hop node information propagation with multi-layer graph neural networks. Substantial experiments reveal that AMOGCN gains superior semi-supervised classification performance compared with state-of-the-art competitors. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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FACOS: Enabling Privacy Protection Through Fine-Grained Access Control with On-chain and Off-chain System

arXiv

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arXiv 2024年

作者： Liu, Chao Hou, Cankun Jiang, Tianyu Ning, Jianting Qiao, Hui Wu, Yusen Fujian Provincial Key Laboratory of Data Intensive Computing the Key Laboratory of Intelligent Computing and Information Processing Fujian China The School of Computer Science and Technology Xidian University Shaanxi Xi’an710071 China The Department of Computer Science University of Miami MiamiFL33146 United States College of Computer and Cyber Security Fujian Normal University Fuzhou350007 China The Faculty of Data Science City University of Macau China

data-driven landscape across finance, government, and healthcare, the continuous generation of information demands robust solutions for secure storage, efficient dissemination, and fine-grained access control. Blockchain technology emerges as a significant tool, offering decentralized storage while upholding the tenets of data security and accessibility. However, on-chain and off-chain strategies are still confronted with issues such as untrusted off-chain data storage, absence of data ownership, limited access control policy for clients, and a deficiency in data privacy and auditability. To solve these challenges, we propose a permissioned blockchain-based privacy-preserving fine-grained access control on-chain and off-chain system, namely FACOS. We applied three fine-grained access control solutions and comprehensively analyzed them in different aspects, which provides an intuitive perspective for system designers and clients to choose the appropriate access control method for their systems. Compared to similar work that only stores encrypted data in centralized or non-fault-tolerant IPFS systems, we enhanced off-chain data storage security and robustness by utilizing a highly efficient and secure asynchronous Byzantine fault tolerance (BFT) protocol in the off-chain environment. As each of the clients needs to be verified and authorized before accessing the data, we involved the Trusted Execution Environment (TEE)-based solution to verify the credentials of clients. Additionally, our evaluation results demonstrated that our system1 offers better scalability and practicality than other state-of-the-art designs. We deployed our system on Alibaba Cloud and Tencent Cloud and conducted multiple evaluations. The results indicate that it takes about 2.79 seconds for a client to execute the protocol for uploading and about 0.96 seconds for downloading. Compared to other decentralized systems, our system exhibits efficient latency for both download and upload operations.

关键词： Access control

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