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学者姓名：杨震
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摘要 ：
Personalized federated learning (PFL) addresses the significant challenge of non-independent and identically distributed (non-IID) data across clients in federated learning (FL). Our proposed framework, "FairDPFL-SCS: Fair Dynamic Personalized Federated Learning with Strategic Client Selection", marks a notable advancement in this domain. By integrating dynamic learning rate adjustments and a strategic client selection mechanism, our approach effectively mitigates the challenges posed by non-IID data while enhancing model personalization, fairness, and efficiency. We evaluated FairDPFL-SCS using standard datasets, including MNIST, FashionMNIST, and SVHN, employing architectures like VGG and CNN. Our model achieved impressive results, attaining 99.04% accuracy on MNIST, 89.19% on FashionMNIST, and 90.9% on SVHN. These results represent a substantial improvement over existing methods, including a highest increase of 16.74% inaccuracy on SVHN when compared to the best-performing benchmark methods. In particular, our method also demonstrated lower fairness variance, presenting the importance of fairness in model personalization, a frequently overlooked aspect in FL research. Through extensive experiments, we validate the superior performance of FairDPFL-SCS compared to benchmark PFL approaches, highlighting significant improvements over state-of-the-art methods. This work represents a promising step forward in the field of federated learning, offering a comprehensive solution to the challenges presented by non-IID data while prioritizing fairness and efficiency in model personalization.
关键词 ：
Client selection Client selection Personalized federated learning Personalized federated learning Dynamic learning Dynamic learning Model optimization Model optimization Fairness Fairness
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| GB/T 7714 | Sabah, Fahad , Chen, Yuwen , Yang, Zhen et al. FairDPFL-SCS: Fair Dynamic Personalized Federated Learning with strategic client selection for improved accuracy and fairness [J]. | INFORMATION FUSION , 2025 , 115 . |
| MLA | Sabah, Fahad et al. "FairDPFL-SCS: Fair Dynamic Personalized Federated Learning with strategic client selection for improved accuracy and fairness" . | INFORMATION FUSION 115 (2025) . |
| APA | Sabah, Fahad , Chen, Yuwen , Yang, Zhen , Raheem, Abdul , Azam, Muhammad , Ahmad, Nadeem et al. FairDPFL-SCS: Fair Dynamic Personalized Federated Learning with strategic client selection for improved accuracy and fairness . | INFORMATION FUSION , 2025 , 115 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Industrial Internet platforms (IIP) can provide many intelligent services based on the industrial big data stored in clouds. However, the vulnerability of cloud storage can cause data corruption, demanding verifying its integrity. Unfortunately, existing cloud storage verification approaches cannot be directly applied to IIP, since they pose heavy computational burdens on the edge side. In this work, we propose an efficient and dynamic storage verification scheme Edasvic for cloud storage in the IIP. We adopt the polynomial commitment to build an efficient homomorphic authenticator, and further design an authenticator accumulator, which can be efficiently generated with limited computational overheads. In addition, we integrate the dynamic information into the authenticator accumulator to support data dynamics. The security of Edasvic is analyzed under the random oracle model. We conduct extensive experiments to evaluate the performance of Edasvic and compare it with the state-of-the-art approaches. Experimental results affirm that Edasvic is superior to existing solutions in terms of computational efficiency.
关键词 ：
Data integrity Data integrity Protocols Protocols Security Security Industrial internet platform Industrial internet platform cloud cloud data dynamics data dynamics Internet Internet Industrial Internet of Things Industrial Internet of Things Polynomials Polynomials efficient verification efficient verification Cloud computing Cloud computing
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| GB/T 7714 | Yu, Haiyang , Zhang, Hui , Yang, Zhen et al. Edasvic: Enabling Efficient and Dynamic Storage Verification for Clouds of Industrial Internet Platforms [J]. | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY , 2024 , 19 : 6896-6909 . |
| MLA | Yu, Haiyang et al. "Edasvic: Enabling Efficient and Dynamic Storage Verification for Clouds of Industrial Internet Platforms" . | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 19 (2024) : 6896-6909 . |
| APA | Yu, Haiyang , Zhang, Hui , Yang, Zhen , Yu, Shui . Edasvic: Enabling Efficient and Dynamic Storage Verification for Clouds of Industrial Internet Platforms . | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY , 2024 , 19 , 6896-6909 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
In the task of multiview multilabel (MVML) classification, each instance is represented by several heterogeneous features and associated with multiple semantic labels. Existing MVML methods mainly focus on leveraging the shared subspace to comprehensively explore multiview consensus information across different views, while it is still an open problem whether such shared subspace representation is effective to characterize all relevant labels when formulating a desired MVML model. In this article, we propose a novel label-driven view-specific fusion MVML method named L-VSM, which bypasses seeking for a shared subspace representation and instead directly encodes the feature representation of each individual view to contribute to the final multilabel classifier induction. Specifically, we first design a label-driven feature graph construction strategy and construct all instances under various feature representations into the corresponding feature graphs. Then, these view-specific feature graphs are integrated into a unified graph by linking the different feature representations within each instance. Afterward, we adopt a graph attention mechanism to aggregate and update all feature nodes on the unified graph to generate structural representations for each instance, where both intraview correlations and interview alignments are jointly encoded to discover the underlying consensuses and complementarities across different views. Moreover, to explore the widespread label correlations in multilabel learning (MLL), the transformer architecture is introduced to construct a dynamic semantic-aware label graph and accordingly generate structural semantic representations for each specific class. Finally, we derive an instance-label affinity score for each instance by averaging the affinity scores of its different feature representations with the multilabel soft margin loss. Extensive experiments on various MVML applications have verified that our proposed L-VSM has achieved superior performance against state-of-the-art methods. The codes are available at https://gengyulyu.github.io/homepage/assets/codes/LVSM.zip.
关键词 ：
Task analysis Task analysis Interviews Interviews Reliability Reliability transformer architecture transformer architecture Correlation Correlation Data models Data models Graph attention mechanism Graph attention mechanism Transformers Transformers multiview multilabel (MVML) learning multiview multilabel (MVML) learning Semantics Semantics multilabel soft margin loss multilabel soft margin loss
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| GB/T 7714 | Lyu, Gengyu , Yang, Zhen , Deng, Xiang et al. L-VSM: Label-Driven View-Specific Fusion for Multiview Multilabel Classification [J]. | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS , 2024 . |
| MLA | Lyu, Gengyu et al. "L-VSM: Label-Driven View-Specific Fusion for Multiview Multilabel Classification" . | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2024) . |
| APA | Lyu, Gengyu , Yang, Zhen , Deng, Xiang , Feng, Songhe . L-VSM: Label-Driven View-Specific Fusion for Multiview Multilabel Classification . | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS , 2024 . |
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摘要 ：
Recommendation methods improve rating prediction performance by learning selection bias phenomenon -users tend to rate items they like. These methods model selection bias by calculating the propensities of ratings, but inaccurate propensity could introduce more noise, fail to model selection bias, and reduce prediction performance. We argue that learning interaction features can effectively model selection bias and improve model performance, as interaction features explain the reason of the trend. Reviews can be used to model interaction features because they have a strong intrinsic correlation with user interests and item interactions. In this study, we propose a preference- and bias -oriented fusion learning model (PBFL) that models the interaction features based on reviews and user preferences to make rating predictions. Our proposal both embeds traditional user preferences in reviews, interactions, and ratings and considers word distribution bias and review quoting to model interaction features. Six realworld datasets are used to demonstrate effectiveness and performance. PBFL achieves an average improvement of 4.46% in root -mean -square error (RMSE) and 3.86% in mean absolute error (MAE) over the best baseline.
关键词 ：
Recommender systems Recommender systems Selection bias Selection bias Text mining Text mining Interaction feature Interaction feature Data mining Data mining
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| GB/T 7714 | Liu, Junrui , Li, Tong , Yang, Zhen et al. Fusion learning of preference and bias from ratings and reviews for item recommendation [J]. | DATA & KNOWLEDGE ENGINEERING , 2024 , 150 . |
| MLA | Liu, Junrui et al. "Fusion learning of preference and bias from ratings and reviews for item recommendation" . | DATA & KNOWLEDGE ENGINEERING 150 (2024) . |
| APA | Liu, Junrui , Li, Tong , Yang, Zhen , Wu, Di , Liu, Huan . Fusion learning of preference and bias from ratings and reviews for item recommendation . | DATA & KNOWLEDGE ENGINEERING , 2024 , 150 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
In the task of multiview multilabel (MVML) classification, each object is described by several heterogeneous view features and annotated with multiple relevant labels. Existing MVML methods usually assume that these heterogeneous features are strictly view-aligned, and they directly conduct cross-view information fusion to train a multilabel prediction model. However, in real-world scenarios, such strict view-aligned requirement can be hardly satisfied due to the recurrent spatiotemporal asynchronism when collecting MVML data, which would cause inaccurate multiview fusion results and degrade the classification performance. To address this issue, we propose a generalized nonaligned MVML (GNAM) classification method, which achieves multiview information fusion while aligning cross-view features and accordingly learns a desired multilabel classifier. Specifically, we first introduce a multiorder matching alignment strategy to achieve cross-view feature alignments, where both first-order feature correspondence and second-order structure correspondence are jointly integrated to guarantee the compactness of the view-alignment results. Afterward, a commonality-and individuality-based multiview fusion structure is formulated on the aligned-view features to excavate the consistencies and complementarities across different views, which leads all relevant multiview semantic labels, especially rare labels, to be characterized more comprehensively. Finally, we embed adaptive global label correlations to multilabel classification model to further enhance its semantic expression integrity and develop an alternative algorithm to optimize the whole model. Extensive experimental results have verified that GNAM is significantly superior to other state-of-the-art methods.
关键词 ：
Adaptation models Adaptation models Correlation Correlation Predictive models Predictive models multiorder matching alignment multiorder matching alignment Task analysis Task analysis Periodic structures Periodic structures commonality and individuality commonality and individuality Learning systems Learning systems Semantics Semantics nonaligned multiview multilabel (MVML) classification nonaligned multiview multilabel (MVML) classification Adaptive label correlations Adaptive label correlations
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| GB/T 7714 | Zhong, Qiyu , Lyu, Gengyu , Yang, Zhen . Align While Fusion: A Generalized Nonaligned Multiview Multilabel Classification Method [J]. | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS , 2024 . |
| MLA | Zhong, Qiyu et al. "Align While Fusion: A Generalized Nonaligned Multiview Multilabel Classification Method" . | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2024) . |
| APA | Zhong, Qiyu , Lyu, Gengyu , Yang, Zhen . Align While Fusion: A Generalized Nonaligned Multiview Multilabel Classification Method . | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS , 2024 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Advanced persistent threat (APT) attack leverages various intelligence-gathering techniques to obtain sensitive and critical information, imposing increasing threats to modern software enterprises. However, due to the persistent presence of APT attacks, it is difficult to effectively analyze a large amount of audit data for detecting such attacks, especially for small and medium-sized enterprises (SMEs). This limitation hinders security operation centers (SOC) from promptly handling APT attacks. In this paper, we propose an attack path-based method (APM) for APT attack detection on few-shot learning. Specifically, APM first identifies candidate malicious entities from the provenance graph, contributing to the completion of the missing attack paths. Secondly, we propose a systematic method to exploit potential attack behaviors in the attack path based on the identified candidate malicious entities. We evaluate APM through five APT attacks in realistic environments. Compared to existing baselines, the precision, recall, and F1-score of APM for attack detection increased by 0.28%, 1.64%, and 1.13%, respectively. The results show that our proposal can outperform baseline approaches and effectively detect APT attacks based on few-shot learning.
关键词 ：
attack detection attack detection provenance graph provenance graph attack path attack path few-shot learning few-shot learning
引用：
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| GB/T 7714 | Li, Jiacheng , Li, Tong , Zhang, Runzi et al. APM: An Attack Path-based Method for APT Attack Detection on Few-Shot Learning [J]. | 2023 IEEE 22ND INTERNATIONAL CONFERENCE ON TRUST, SECURITY AND PRIVACY IN COMPUTING AND COMMUNICATIONS, TRUSTCOM, BIGDATASE, CSE, EUC, ISCI 2023 , 2024 : 10-19 . |
| MLA | Li, Jiacheng et al. "APM: An Attack Path-based Method for APT Attack Detection on Few-Shot Learning" . | 2023 IEEE 22ND INTERNATIONAL CONFERENCE ON TRUST, SECURITY AND PRIVACY IN COMPUTING AND COMMUNICATIONS, TRUSTCOM, BIGDATASE, CSE, EUC, ISCI 2023 (2024) : 10-19 . |
| APA | Li, Jiacheng , Li, Tong , Zhang, Runzi , Wu, Di , Yue, Hao , Yang, Zhen . APM: An Attack Path-based Method for APT Attack Detection on Few-Shot Learning . | 2023 IEEE 22ND INTERNATIONAL CONFERENCE ON TRUST, SECURITY AND PRIVACY IN COMPUTING AND COMMUNICATIONS, TRUSTCOM, BIGDATASE, CSE, EUC, ISCI 2023 , 2024 , 10-19 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
在电商评论场景下的一种基于生成对抗网络的文本对抗样本防御方法属于对抗样本领域，包含训练阶段、攻击阶段及防御阶段。训练阶段包含步骤：1.获取目标模型的输入数据与预测标签；2.依据真实数据和对应标签，借助攻击方法，训练生成扰动的生成器；3.扰动和原始数据混合，得到对抗样本；4.真实样本和对抗样本同时输入判别器，训练鉴别生成数据的判别器。攻击阶段包含步骤：1.生成对抗样本；2.和真实样本混合，输入目标模型；3.目标模型预测准确率。防御阶段包含步骤：1.生成对抗样本；2.和真实样本混合；3.判别器作为过滤器，过滤对抗样本；4.目标模型预测准确率。本发明生成更加真实的对抗样本数据，能够非常快速、简单的得到对应的防御方法。
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| GB/T 7714 | 马帅 , 于海阳 , 杨震 . 在电商评论场景下的一种基于生成对抗网络的文本对抗样本防御方法 : CN202310003911.9[P]. | 2023-01-03 . |
| MLA | 马帅 et al. "在电商评论场景下的一种基于生成对抗网络的文本对抗样本防御方法" : CN202310003911.9. | 2023-01-03 . |
| APA | 马帅 , 于海阳 , 杨震 . 在电商评论场景下的一种基于生成对抗网络的文本对抗样本防御方法 : CN202310003911.9. | 2023-01-03 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
本发明公开了一种面向工业物联网的数据验证方法，该方法包括：首先工业物联网设备为整个系统生成秘密值和公共参数。然后，它使用每个文件块计算文件多项式，并进一步计算同态标签。最后，它为同态标签构造默克尔树并且生成根哈希。工业物联网设备将根哈希发送给验证者，并将文件和同态标签发送给云服务器。云服务器从工业物联网设备接收到同态标签和文件后，将根据验证者挑战的集合生成包括多项式承诺和辅助认证信息在内的证明。最后，验证者将同时验证辅助认证信息和多项式承诺。本方法采用结合多项式承诺的同态哈希函数，解决了现有的工业云平台上数据完整性验证不适用于计算和存储能力有限的工业物联网设备的问题。
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| GB/T 7714 | 张辉 , 于海阳 , 杨震 . 一种面向工业物联网的数据验证方法 : CN202310150061.5[P]. | 2023-02-22 . |
| MLA | 张辉 et al. "一种面向工业物联网的数据验证方法" : CN202310150061.5. | 2023-02-22 . |
| APA | 张辉 , 于海阳 , 杨震 . 一种面向工业物联网的数据验证方法 : CN202310150061.5. | 2023-02-22 . |
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摘要 ：
本发明公开了基于分布式存储的高效双压缩数据完整性验证方案。流程如下：首先，数据所有者将数据分块后进行双重压缩，第一次压缩是通过将数据块分组哈希，第二次压缩是将压缩后的数据块转变为多项式。然后数据所有者将压缩后的数据块上传到分布式存储节点中，同时上传数据认证器和审计状态。当验证者要验证分布式存储节点的存储状态时，验证者发送随机挑战给分布式存储节点用于检查存储状态。分布式存储节点根据发送的挑战内容计算相应的证明，并将证明发送给验证者。验证者根据已有参数进行验证确认数据的完整性与安全性。此外，为了进一步提升安全性，本发明实现了链上审计，主要是将验证者替换为了区块链上的智能合约。
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| GB/T 7714 | 于海阳 , 陈雨润 , 杨震 . 基于分布式存储的高效双压缩数据完整性验证方案 : CN202310821315.1[P]. | 2023-07-06 . |
| MLA | 于海阳 et al. "基于分布式存储的高效双压缩数据完整性验证方案" : CN202310821315.1. | 2023-07-06 . |
| APA | 于海阳 , 陈雨润 , 杨震 . 基于分布式存储的高效双压缩数据完整性验证方案 : CN202310821315.1. | 2023-07-06 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
本发明公开了基于Cutmix数据增强的联邦学习隐私保护方法，应用于对医院影像数据中心进行隐私保护的场景。医院数据中心的中央服务器在联邦训练开始前，确定一个深度学习模型作为各客户端训练的模型；联邦训练开始后，中央服务器会将此模型下发给各客户端；为了在训练过程中图像不存在无信息像素，本发明在对原始数据进行保护时，采用基于Cutmix数据增强方法，利用数据增强策略在图片生成方面的优势，使训练模型学习到更多的鲁棒性特征，有效提高模型的泛化能力。本发明针对联邦学习中的梯度反演攻击进行防御，增强对梯度反演攻击过程的约束，提出对联邦学习更安全的防御场景。本方法可以在少量数据效用损失的情况下防御最先进的攻击。
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| GB/T 7714 | 李颖阁 , 陈渝文 , 杨震 . 一种基于Cutmix数据增强的联邦学习隐私保护方法 : CN202310586380.0[P]. | 2023-05-24 . |
| MLA | 李颖阁 et al. "一种基于Cutmix数据增强的联邦学习隐私保护方法" : CN202310586380.0. | 2023-05-24 . |
| APA | 李颖阁 , 陈渝文 , 杨震 . 一种基于Cutmix数据增强的联邦学习隐私保护方法 : CN202310586380.0. | 2023-05-24 . |
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