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Application Research of Vertical Federated Learning Technology in Banking Risk Control Model Strategy

Application Research of Vertical Federated Learning Technolo...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Yong Luo Zhi Lu Xiaofei Yin Songfeng Lu Yiting Weng Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China China Minsheng Banking Corp. Ltd. Beijing China Shenzhen Huazhong University of Science and Technology Research Institute Wuhan China Shu Lun Pan Beijing China

This study centers on the application of vertical federated learning technology in the context of Internet banking loans, with a particular focus on innovations in data privacy protection, risk control model algorithms, and secure multi-party computation. Currently, banking risk control strategies mainly rely on traditional data processing technologies, which often fall short in protecting user privacy and ensuring data usage efficiency. We adopt vertical federated learning technology, offering an innovative solution for the Internet banking loan scenario. Firstly, regarding data privacy protection, we propose a differential privacy mechanism to safeguard user-sensitive data. Secondly, we innovatively apply risk control model algorithms, facilitating collaborative modeling across multiple Internet loan platforms through federated learning. Furthermore, we introduce secure multi-party computation technology to ensure the secure transmission of data and confidentiality of computation processes during federated learning. Through empirical experiments on real Internet loan datasets, we validate the effectiveness and feasibility of our proposed methods. After implementing our risk control model, the credit approval rate increased from 3.44% to 18.2%, with a single-day high reaching 25.53%. The average loan amount increased by 7,700 yuan, and the average interest rate slightly declined by 0.48%, marking a significant improvement and breakthrough compared to traditional risk control models. This study offers innovative solutions for data privacy protection and risk control in the Internet loan scenario, providing safer and more reliable services for financial institutions and users. Moreover, our methods possess high practicality and promotional value. The potential widespread impact on the industry is profound.

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Unveiling the Effects of Experience Replay on Deep Reinforcement Learning-based Power Allocation in Wireless Networks

Unveiling the Effects of Experience Replay on Deep Reinforce...

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IEEE conference on Wireless Communications and Networking

作者： Amna Kopic Erma Perenda Haris Gacanin Chair for Distributed Signal Processing RWTH Aachen University Aachen Germany

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Deep reinforcement learning has emerged as a pow-erful tool for dynamic power allocation, as it allows continuous learning and realtime responsiveness to environmental changes through its core element, experience replay. Experience replay involves two critical hyperparameters: the replay buffer and mini-batch sizes. While state-of-the-art solutions primarily concentrate on designing input features and reward-shaping methods, the impact of experience replay parameters on system performance has often been overlooked. This paper aims to address this gap by exploring the effects of experience replay parameters in the context of dynamic power allocation in multi-carrier wireless systems. To address the power allocation problem, we propose a multi-agent cooperative deep reinforcement learning framework. The results show that a minimum of 2000 experiences in the replay buffer is necessary for the proposed solution to outperform conventional approaches. Moreover, many obsolete experiences within a larger replay buffer slightly decrease system performance. Interestingly, the increase in batch size does not significantly affect the learning models' training time due to parallel execution, yet, it improves performance.

关键词： Training Correlation System performance Wireless networks Computational modeling Dynamic scheduling Deep reinforcement learning

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Identification of rolling stock of railways based on multi-projection image processing methods 7

Identification of rolling stock of railways based on multi-p...

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7th International conference "Information Technology and Interactions", IT and I 2020

作者： Bilan, Stepan State University of Infrastructure and Technology Kyrylivska 9 Kyiv04071 Ukraine Taras Shevchenko National University of Kyiv Volodymyrska Street 60 Kyiv01033 Ukraine

The paper considers the method for identifying railway rolling stock based on the parallel shift technology and Radon transformation using cellular automata. The main stages of the method are considered, which consist of: fixing a moving object, extracting an identifier image, removing noise, extracting characteristic features and comparing with a standard. To fix the rolling stock, the selection of pixels is used, which have changed their properties on adjacent frames, taking into account the sensitivity and speed of movement. The selected pixels are used to select a rectangular window containing a license plate. To remove noise in the selected image, the parallel shift technology and the Radon transform, implemented on cellular automata with a hexagonal covering, are used. The method is implemented for images subject to various distortions that affect the identification result. © 2020 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Pixels

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GDTNet: A Synergistic Dilated Transformer and CNN by Gate Attention for Abdominal Multi-organ Segmentation 30th

GDTNet: A Synergistic Dilated Transformer and CNN by Gate ...

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30th International conference on MultiMedia Modeling, MMM 2024

作者： Zhang, Can Wang, Zhiqiang Zhang, Yuan Li, Xuanya Hu, Kai Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan411105 China Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province Xiangnan University Chenzhou423000 China Baidu Inc. Beijing100085 China

ISBN: (纸本)9783031533013

As one of the key problems in computer-aided medical image analysis, learning how to model global relationships and extract local details is crucial to improve the performance of abdominal multi-organ segmentation. While current techniques for Convolutional Neural Networks (CNNs) are quite mature, their limited receptive field makes it difficult to balance the ability to capture global relationships with local details, especially when stacked onto deeper networks. Thus, several recent works have proposed Vision Transformer based on a self-attentive mechanism and used it for abdominal multi-organ segmentation. However, Vision Transformer is computationally expensive by modeling long-range relationships on pairs of patches. To address these issues, we propose a novel multi-organ segmentation framework, named GDTNet, based on the synergy of CNN and Transformer for mining global relationships and local details. To achieve this goal, we innovatively design a Dilated Attention Module (DAM) that can efficiently capture global contextual features and construct global semantic information. Specifically, we employ a three-parallel branching structure to model the global semantic information of multiscale encoded features by Dilated Transformer, combined with global average pooling under the supervision of Gate Attention. In addition, we fuse each DAM with DAMs from all previous layers to further encode features between scales. Extensive experiments on the Synapse dataset show that our method outperforms ten other state-of-the-art segmentation methods, achieving accurate segmentation of multiple organs in the abdomen. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Computer aided analysis

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Asynchronous Optimization methods for Efficient Training of Deep Neural Networks with Guarantees 35

Asynchronous Optimization Methods for Efficient Training of ...

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

作者： Kungurtsev, Vyacheslav Egan, Malcolm Chatterjee, Bapi Alistarh, Dan Czech Tech Univ Dept Comp Sci Fac Elect Engn Prague Czech Republic Univ Lyon INSA Lyon INRIA Lyon France IST Austria Klosterneuburg Austria

ISBN: (纸本)9781577358664

Asynchronous distributed algorithms are a popular way to reduce synchronization costs in large-scale optimization, and in particular for neural network training. However, for nonsmooth and nonconvex objectives, few convergence guarantees exist beyond cases where closed-form proximal operator solutions are available. As training most popular deep neural networks corresponds to optimizing nonsmooth and nonconvex objectives, there is a pressing need for such convergence guarantees. In this paper, we analyze for the first time the convergence of stochastic asynchronous optimization for this general class of objectives. In particular, we focus on stochastic subgradient methods allowing for block variable partitioning, where the shared model is asynchronously updated by concurrent processes. To this end, we use a probabilistic model which captures key features of real asynchronous scheduling between concurrent processes. Under this model, we establish convergence with probability one to an invariant set for stochastic subgradient methods with momentum. From a practical perspective, one issue with the family of algorithms that we consider is that they are not efficiently supported by machine learning frameworks, which mostly focus on distributed data-parallel strategies. To address this, we propose a new implementation strategy for shared-memory based training of deep neural networks for a partitioned but shared model in single- and multi-GPU settings. Based on this implementation, we achieve on average about 1.2x speed-up in comparison to state-of-the-art training methods for popular image classification tasks, without compromising accuracy.

关键词： Deep neural networks

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MVDiffusion: enabling holistic multi-view image generation with correspondence-aware diffusion 23

MVDiffusion: enabling holistic multi-view image generation w...

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Proceedings of the 37th International conference on Neural Information processing Systems

作者： Shitao Tang Fuyang Zhang Jiacheng Chen Peng Wang Yasutaka Furukawa Simon Fraser University Bytedance

This paper introduces MVDiffusion, a simple yet effective method for generating consistent multi-view images from text prompts given pixel-to-pixel correspondences (e.g., perspective crops from a panorama or multi-view images given depth maps and poses). Unlike prior methods that rely on iterative image warping and inpainting, MVDiffusion simultaneously generates all images with a global awareness, effectively addressing the prevalent error accumulation issue. At its core, MVDiffusion processes perspective images in parallel with a pre-trained text-to-image diffusion model, while integrating novel correspondence-aware attention layers to facilitate cross-view interactions. For panorama generation, while only trained with 10k panoramas, MVDiffusion is able to generate high-resolution photorealistic images for arbitrary texts or extrapolate one perspective image to a 360-degree view. For multi-view depth-to-image generation, MVDiffusion demonstrates state-of-the-art performance for texturing a scene mesh. The project page is at https://***/.

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Prediction and Evaluation Method of Modification Effect of Large-Scale DBD Insulation Materials Based on distributed Current Measurement and Neural Network Model 1st

Prediction and Evaluation Method of Modification Effect of L...

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1st Electrical Artificial Intelligence conference, EAIC 2024

作者： Yizhuo, Wang Zhonlian, Li Long, Li Runhua, Li Xinglei, Cui Zhi, Fang School of Electrical Engineering and Control Science Nanjing Tech University Nanjing211816 China

ISBN: (纸本)9789819648559

Wide dielectric barrier discharge (DBD) has broad application prospects in the modification of insulating materials, but the aging of the electrode directly affects the modification effect in the application process. As the size of the DBD device increases, the real-time evaluation of its modification effect becomes more complicated. Therefore, this paper proposes a real-time prediction and evaluation method for the modification effect of wide DBD insulation materials based on distributed current measurement and neural network model. The operating condition parameters such as DBD excitation voltage amplitude, repetition frequency, discharge working gas flow rate and reaction medium flow rate were changed. The discharge current at different positions was measured by self-made current coil. The water contact angle and flashover voltage at the corresponding position were tested experimentally as the evaluation criteria of the modification effect. The feature extraction of distributed current is carried out by manual and image recognition methods, and the prediction and evaluation models are established by BP neural network and convolutional neural network (CNN) respectively. The accuracy and generalization ability of the two models are compared. The results show that the CNN model based on image recognition has higher accuracy and generalisation ability in predicting the water contact angle and flashover voltage on the material surface compared to the BP neural network model based on manual feature extraction. Compared with the BP neural network, the CNN model reduces the mean absolute error (MAE) by 41.3% and the root mean square error (RMSE) by 36.1% in predicting the water contact angle of the material surface, and the mean absolute error (MAE) reduces by 47.7% and the RMSE reduces by 40.2% in the flashover voltage. The experimental results with different processing distances are used to examine the generalisation ability of the two models, and the results show that

关键词： Electric insulating materials

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Development of methods for parallel processing of series of images obtained by a machine vision system in various electromagnetic ranges 2

Development of methods for parallel processing of series of ...

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conference on Optical Instrument Science, Technology, and Applications II Held as Part of SPIE Optical System Design conference

作者： Semenishchev, Evgenii Voronin, Viacheslav Alepko, Andrey Zelensky, Aleksandr Agaian, Sos Moscow State Tech Univ STANKIN Moscow Russia CUNY Dept Comp Sci New York NY 10314 USA

ISBN: (数字)9781510645974

ISBN: (纸本)9781510645974;9781510645967

Machine vision systems used in modern industrial complexes, based on the analysis of multi and hyperspectral imaging. The transition to implementing the "Industry 4.0" program is not possible when using one type of data. The first control system used only the visible range image. They made it possible to analyze the trajectories of movement of objects, control product quality, carry out security functions (control of perimeter crossing), etc. The development of new industrial robotic cells and processing complexes using cognitive functions implying the receipt, analysis, and processing of heterogeneous data. The construction of a unified information field, which allows performing multidimensional operations with data, allows increasing the speed of decision-making and the implementation of automated robot-human systems at the level of an assistant working in a unified workspace. The use of machine vision systems analyzing information received in: visible (shape, the trajectory of movement, position of objects, etc.);near-infrared range (data is similar to visible, allows operation in dusty, foggy, low light conditions);far-infrared range - thermal (plotting temperature gradients, identifying areas of overheating);ultraviolet range (analysis of ionization sources, corona discharges, static charges, tags);X-ray and microwave ranges (analysis of the surface and internal structure of objects, allow the identification of defects);range and 3D sensors (construction of volumetric figures, analysis of the relative position of objects and their interaction), etc. Data analysis is often performed not by a single camera but by a group of sensors located not in a single housing. Primary data integration reduces the number of information channels while maintaining the functionality and accuracy of the analysis. The article discusses creating fusion images obtained by industrial sensors into a combined image containing joint data. Combining multi and hyperspectral imaging makes i

关键词： preprocessing IR image local features denoising combining image fusion image

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WBA: A Warping-based Approach to Generating Imperceptible Adversarial Examples

WBA: A Warping-based Approach to Generating Imperceptible Ad...

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IEEE International conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Chengyao Hua Shigeng Zhang Weiping Wang Zhankai Li Jian Zhang School of Computer Science and Engineering Central South University Changsha China Science and Technology on Parallel and Distributed Processing Laboratory (PDL)

The human can easily recognize the incongruous parts of an image, for example, perturbations unrelated to the image itself, but are poor at spotting the small geometric transformations. However, in terms of the robustness of deep neural networks (DNNs), the ability to properly recognize objects with small geometric transformations is still a challenge. In this work, we investigate the problem from the perspective of adversarial attacks: does the performance of DNNs degrade even when small geometric transformations are applied to images? To this end, we propose a novel adversarial attack method, called WBA, a Warping-Based Adversarial attack method, which does not introduce information independent of the original images but manipulates the existing pixels of the images by elastic warping transformations to generate adversarial examples that are imperceptible to the human eye. At the same time, existing adversarial attacks typically generate adversarial examples by modifying pixels in the spatial domain of the image, the addition of such perturbations introduces extra information unrelated to the image itself and is easily detected by the naked eyes. We demonstrate the effectiveness of WBA by extensive experiments on commonly used datasets, including MNIST, CIFAR10, and imageNet. The results show that WBA can quickly generate adversarial examples with the highest adversarial strength, consumes less time, and can be comparable to optimization-based adversarial attack methods in image perception evaluation metrics such as LPIPS, SSIM, and far more than gradient direction-based iterative methods.

关键词： Measurement Deep learning Privacy image recognition Perturbation methods Neural networks Robustness

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BLOCK distributed 3MG ALGORITHM AND ITS APPLICATION TO 3D image RESTORATION

BLOCK DISTRIBUTED 3MG ALGORITHM AND ITS APPLICATION TO 3D IM...

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IEEE International conference on image processing (ICIP)

作者： Chalvidal, Mathieu Chouzenoux, Emilie Univ Paris Saclay CVN INRIA Cent Supelec F-91190 Gif Sur Yvette France

ISBN: (纸本)9781728163956

Modern 3D image recovery problems require powerful optimization frameworks to handle high dimensionality while providing reliable numerical solutions in a reasonable time. In this perspective, asynchronous parallel optimization algorithms have received an increasing attention by overcoming memory limitation issues and communication bottlenecks. In this work, we propose a block distributed Majorize-Minorize Memory Gradient (BD3MG) optimization algorithm for solving large scale non-convex differentiable optimization problems. Assuming a distributed memory environment, the algorithm casts the efficient 3MG scheme [1] into smaller dimension subproblems where blocks of variables are addressed in an asynchronous manner. Convergence of the sequence built by the proposed BD3MG method is established under mild assumptions. Application to the restoration of 3D images degraded by a depth-variant blur shows that our method yields significant computational time reduction compared to several synchronous and asynchronous competitors, while exhibiting great scalability potential.

关键词： Majorization-Minimization Block-alternating optimization distributed scheme Asynchronous communication image deblurring Depth-varying blur

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