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检索条件"任意字段=25th International Conference on Intelligent Data Engineering and Automated Learning"

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引用

25th international conference on Web Information Systems engineering, WISE 2024

作者： Tu, Longchuan Meng, Shunmei Liu, Xiao Liu, Guanfeng Beheshti, Amin Zhang, Xuyun School of Computer Science and Engineering Nanjing University of Science and Technology Nanjin China State Key Laboratory for Novel Software Technology Nanjing University Nanjin China School of Computing Macquarie University Sydney Australia

ISBN: (纸本)9789819605699

Recently, graph neural networks(GNNs) have played a key crucial in many recommendation situations. In particular, contrastive learning-based hypergraph neural networks (HGNNs) are gradually becoming a research focus for addressing issues of data sparsity and noise. Despite many studies proving their outstanding performance, there are still some shortcomings: i) Most contrastive learning-based HGNNs primarily rely on cross-view contrastive learning, while neglecting contrastive learning on the interaction graph. ii) Utilizing node embedding for hypergraph structure learning is susceptible to the influence of low-quality representations, thereby constraining the learning capability of HGNNs. To address these issues, we offer a Semantic Similarity-based Graph Contrastive learning framework (SSGCL), which aims to jointly learn representations with rich semantic information through within-view and cross-view contrastive learning. Specifically, we first introduce consistency contrastive learning, which captures self-supervised signals through semantic similarity between nodes and their neighborhoods. It better captures the unique features of nodes through the connections between nodes and their neighborhoods. then, we utilize interaction graph learning hypergraph structure, promoting it to extract potential node connections and thus improving its ability to describe homogeneous node relationships. Experimental evaluations on three actual datasets show that SSGCL performs much better than the current baseline models. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive learning

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SARA: A Sparsity-Aware Efficient Oblivious Aggregation Service for Federated Matrix Factorization 25th

SARA: A Sparsity-Aware Efficient Oblivious Aggregation Servi...

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25th international conference on Web Information Systems engineering, WISE 2024

作者： Zheng, Yifeng Xiong, Tianchen Ouyang, Huajie Wang, Songlei Hua, Zhongyun Gao, Yansong School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Data61 CSIRO Sydney Australia

ISBN: (纸本)9789819605668

Federated matrix factorization (FedMF) has recently emerged as a privacy-friendly paradigm which runs matrix factorization (MF) in a federated learning (FL) setting and enables users to keep their individual rating data local in the training process. In FedMF, users only need to share out item gradients for aggregation. However, prior work has shown that the item gradients, if directly exposed, can still leak users’ rating data. Meanwhile, the rating data are typically sparse and simply uploading the gradients of rated items will leak which and how many items a user rates. In light of the above, in this paper, we present SARA, a new sparsity-aware efficient oblivious aggregation service for FedMF. SARA protects the confidentiality of item gradients as well as hides which and how many items a user rates, through a custom sparsity-aware design that delicately builds on differential privacy and lightweight cryptography. Extensive experiments over real-world datasets demonstrate that SARA is utility-preserving and can bring the users significant savings (up to 98.28%) in the number of transmitted item gradients, compared to the baseline of fully transmitting gradients for all possible items. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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TERM: Tree Ensemble Models for Interpretable Rule Mining 25th

TERM: Tree Ensemble Models for Interpretable Rule Mining

引用

25th international conference on Web Information Systems engineering, WISE 2024

作者： Zhu, Mengyu Li, Zhao Zhao, Jun Liu, Xiaopeng Liu, Yongning School of Information Engineering Ningxia University Yinchuan China College of Intelligence and Computing Tianjin University Tianjin China School of Economics and Management Ningxia University Yinchuan China Financial Big Data Laboratory Bank of Shizuishan Yinchuan China

ISBN: (纸本)9789819605781

Ensemble learning, particularly tree-based ensemble techniques, is acknowledged as the advanced approach to solving a wide range of challenging issues because of its exceptional performance in multiple machine learning applications. Nonetheless, even with its capacity for prediction, ensemble learning’s interpretability continues to be a problem, which restricts its use in situations where decision transparency is necessary. We suggest a novel model named TERM (Tree Ensemble Models for Interpretable Rule Mining) in order to get over this restriction and enhance the interpretability of Ensemble Tree Models. this method converts the complex binary classification decision-making process inside tree ensemble models into a series of rule lists that are globally equivalent to the decision-making process of the original model. this transformation aids in a more precise understanding and interpretation of the model’s predictive outcomes, enhancing its interpretability. Compared with existing rule extraction methods, TERM can simultaneously be applied to bagging methods and boosting methods. During the rule extraction phase, an adaptive set cover algorithm ADAPT-TA, is proposed to select rule subsets from the original rule set while avoiding local optima. Additionally, this method reduces the generalization error in the set of rules and runs faster on ensemble models involving multiple decision trees. through comparisons with six other models, we evaluated the performance and applicability of TERM, demonstrating its outstanding accuracy and interpretability. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive learning

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Model Extraction Attacks on Privacy-Preserving Deep learning Based Medical Services 25th

Model Extraction Attacks on Privacy-Preserving Deep Learnin...

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25th international conference on Web Information Systems engineering, WISE 2024

作者： Wang, Xinqian Liu, Xiaoning Yi, Xun Yang, Xuechao Gondal, Iqbal RMIT University Melbourne Australia Victoria University Melbourne Australia

ISBN: (纸本)9789819605668

Advancements in deep learning have enabled high-quality medical diagnostic services. Typically, high-tech corporations train deep learning models to provide these services to their clients. To access services, clients simply upload their medical data and receive predictions. However, due to the sensitivity of medical data, such services usually face privacy concerns from their clients. Additionally, service providers cannot directly deploy their models on clients’ devices, as the trained models are considered valuable intellectual property. To address this issue, secure multi-party computation (MPC) has been proposed as a solution, allowing service providers and clients to jointly perform deep learning computations without disclosing their respective inputs. For efficiency reasons, most MPC-based secure inference protocols assume that all parties will remain semi-honest throughout the execution of the protocols. However, this assumption may not always hold for clients. A recent study has shown that if a client is compromised by a malicious adversary, the adversary can steal the model’s parameters. However, such attack can be performed across limited neural network structures that only combines linear layers and ReLU layers. In this paper, we extend the attack to be applicable to most neural network structures commonly used in practice, e.g., VGG-Net and ResNet. Specifically, we design malicious steps for the adversary to circumvent max-pool and softmax layers. Our experimental results demonstrate that the extracted model parameters differ from the true values by at most 0.01. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Differential privacy

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VizardFL: Enabling Private Participation in Federated learning Systems 25th

VizardFL: Enabling Private Participation in Federated Learn...

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25th international conference on Web Information Systems engineering, WISE 2024

作者： Zang, Yichen Cai, Chengjun Dong, Wentao Wang, Cong City University of Hong Kong Kowloon Hong Kong Guangdong Dongguan China

ISBN: (纸本)9789819605668

We introduce the problem of private participation in federated learning (FL) systems. In this problem, different data owners can participate in different FL training tasks without revealing exactly which task they are involved in. It is extremely important in some metadata-sensitive scenarios (e.g., a patient does not want to disclose the fact that he/she is diseased but wants to contribute to the disease study). Despite the inherent privacy assurance of conventional FL techniques and recent advances in secure aggregations, such private participation remains an open issue. this work introduces VizardFL, an FL framework that efficiently enables private participation. At a high level, VizardFL is built out of distributed trust across two servers that keep client participation private as long as there is no collusion. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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MDAP: A Multi-view Disentangled and Adaptive Preference learning Framework for Cross-Domain Recommendation 25th

MDAP: A Multi-view Disentangled and Adaptive Preference Lea...

引用

25th international conference on Web Information Systems engineering, WISE 2024

作者： Tong, Junxiong Yin, Mingjia Wang, Hao Pan, Qiushi Lian, Defu chen, Enhong University of Science and Technology of China Jinzhai Road Anhui Hefei China State Key Laboratory of Cognitive Intelligence Anhui Hefei China

ISBN: (纸本)9789819605699

Traditional recommendation systems, relying on single-domain data, often struggle with sparse data or new user scenarios. Cross-domain Recommendation (CDR) systems leverage multi-domain user interactions to improve performance, especially in sparse data or new user scenarios. However, CDR faces challenges such as effectively capturing user preferences and avoiding negative transfer. To address these issues, we propose the Multi-viewDisentangled andAdaptivePreference learning (MDAP) framework. Our MDAP framework uses a multi-view encoder to capture diverse user preferences. the framework includes a gated decoder that adaptively combines embeddings from different views to generate a comprehensive user representation. By disentangling representations and allowing adaptive feature selection, our model enhances recommendations’ adaptability and effectiveness. Extensive experiments on benchmark datasets demonstrate that our method significantly outperforms state-of-the-art CDR and single-domain models, providing more accurate recommendations and deeper insights into user behavior across different domains. Our code is available at https://***/the-garden-of-sinner/MDAP. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Recommender systems

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R-CONV: An Analytical Approach for Efficient data Reconstruction via Convolutional Gradients 25th

R-CONV: An Analytical Approach for Efficient Data Reconstru...

引用

25th international conference on Web Information Systems engineering, WISE 2024

作者： Eltaras, Tamer Ahmed Malluhi, Qutaibah Savino, Alessandro Carlo, Stefano Di Qayyum, Adnan Politecnico Di Torino Control and Computer Engineering Department Turin10129 Italy Qatar University Doha Qatar Information Technology University of the Punjab Lahore Pakistan

ISBN: (纸本)9789819605750

In the effort to learn from extensive collections of distributed data, federated learning has emerged as a promising approach for preserving privacy by using a gradient-sharing mechanism instead of exchanging raw data. However, recent studies show that private training data can be leaked through many gradient attacks. While previous analytical-based attacks have successfully reconstructed input data from fully connected layers, their effectiveness diminishes when applied to convolutional layers. this paper introduces an advanced data leakage method to efficiently exploit convolutional layers’ gradients. We present a surprising finding: even with non-fully invertible activation functions, such as ReLU, we can analytically reconstruct training samples from the gradients. To the best of our knowledge, this is the first analytical approach that successfully reconstructs convolutional layer inputs directly from the gradients, bypassing the need to reconstruct layers’ outputs. Prior research has mainly concentrated on the weight constraints of convolution layers, overlooking the significance of gradient constraints. Our findings demonstrate that existing analytical methods used to estimate the risk of gradient attacks lack accuracy. In some layers, attacks can be launched with less than 5 % of the reported constraints. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Semantic Communication of Images Using Image Generation and Image Captioning Models

Semantic Communication of Images Using Image Generation and ...

引用

PhD Symposium, Posters, Demos, and A Web for more inclusive, sustainable and prosperous societies, WEB-for-GOOD 2024 and 1st international Workshop on AI and Web data Analytics, AIWDA 2024 form the 25th international conference on Web Information Systems engineering, WISE 2024

作者： Mahgoub, Asma Yaacoub, Elias Department of Computer Science and Engineering Qatar University Doha Qatar

ISBN: (纸本)9789819614820

With the emergence of 6G networks and the new networking requirements, the existing networks are approaching the Shannon capacity limit. Hence, a new paradigm called ‘semantic communication’ is proposed in the literature. Semantic communication is the transmission of the meaning of a message between two intelligent agents. In this work, the problem of image semantic communication is considered. Most of the existing works in literature focus on the exact reconstruction of the transmitted image. Yet, to achieve full semantic communication, it is possible to focus on transmitting a similar image with the same meaning as the original image. this can be achieved by transmitting a text description of the image. An image captioning model, namely, the one-for-all (OFA) model is used to transform an image into its caption. the caption is transmitted through the network and at the receiver an image-to-text diffusion model, stability diffusion XL, is used to reconstruct a semantically similar image. the similarity between the transmitted and the received images is assessed using the textual similarity between the corresponding captions. the captions of the original and the generated images had a similarity above 0.7 using five text similarity measures, namely, ANLS, METEOR, ROUGE, BLEU and BERT similarity. this approach is shown to achieve a 99% reduction in the message size. the results emphasize the effect of using AI models to extract a general representation of a message and in turn consume less communication resources. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Chatbots

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Fed-MoE: Efficient Federated learning for Mixture-of-Experts Models via Empirical Pruning 25th

Fed-MoE: Efficient Federated Learning for Mixture-of-Expert...

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25th international conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2024

作者： Zou, Yifei Qi, Senmao Yuan, Yuan Wang, Dawei Shen, Shikun Wu, Libing Guo, Shaoyong Yu, Dongxiao School of Computer Science and Technology Shandong University Qingdao China School of Cyber Science and Engineering Wuhan University Wuhan China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9789819642069

In recent years, the combination of Federated learning (FL) framework and Mixture of Experts (MoE) architecture has shown promise for large-scale model pre-training while preserving data privacy. However, the significant computational and communication requirement of MoE-based models presents substantial challenges for FL systems. To address this, we propose Fed-MoE, an efficient federated learning framework designed for MoE models through empirical pruning. Fed-MoE reduces computational overhead by pruning multiple experts at the client side while retaining general components, thus converting the model into dense sub-models. these sub-models are locally trained and later aggregated by the parameter server into a sparse MoE model. Our method optimizes the use of sparse activation in MoE models and reduces resource consumption without compromising performance. Extensive experimental results demonstrate that Fed-MoE significantly improves the efficiency of FL without compromising model performance, offering an effective solution for deploying large-scale MoE models in federated learning environments. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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An automated Diagnosis of Diabetic Retinopathy Grading Using DenseNet169 17

An Automated Diagnosis of Diabetic Retinopathy Grading Using...

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17th IEEE international conference on Computer Research and Development, ICCRD 2025

作者： Yang, Cheng-Chung Li, Na Kwame Bensah, Kulevome Delanyo Khokhar, Salah-Ud-Din Mahrukh, Mahrukh Qilu Institute of Technology School of Intelligent Manufacturing and Control Engineering Jinan China

ISBN: (纸本)9798331531881

this study used DenseNet121, DenseNet169, DenseNet201, EfficientNetB0, EfficientNetB7, Inception ResNetV2, Inception V3, MobileNet, MobileNetV2, MobileNetV3Large, ResNet50, ResNet101 and ResNet152, 13 different deep learning neural network models were used for automatic grading of diabetes retinopathy(DR). the change of Validation Accuracy trend was initially observed with 10, 20, 30, 40 and 50, different EPOCHs. Before grading, pre-processing such as elimination, amplification, denoising, data enhancement, normalization, etc. were carried out for the problems in the retinal image data set in the experiment. then we used the deep learning neural network model proposed in this study to embed the attention mechanism into DenseNet169 for automatic grading of DR, enable deep learning neural network models to focus on features such as exudates, thick blood vessels and microaneurysms in retinal images. In order to avoid vanishing gradients in the neural network model and ensure classification accuracy, DenseNet169 was adjusted by using a targeted swish function for processing, and classify retinal images into three levels and finally the classification accuracy of the neural network model proposed in this study reached 99.76%, macro avg precision reached 99.76%, macro avg recall reached 99.76% and macro avg f1-score reached 99.76%. It is significantly better than other models and on average. It can correctly identify and automatically grading more than 4600 images in the verification of 4,617 retinal images. the method proposed in this study has been validated to effectively improve the accuracy of DR classification and can be used for large-scale DR medical imaging screening. It can effectively assist doctors in achieving efficient diagnosis of DR grading. this work was supported by Research Program of Qilu Institute of Technology (***24NN032). © 2025 IEEE.

关键词： Deep neural networks

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