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FedPHE: A Secure and Efficient Federated Learning via Packed Homomorphic Encryption

IEEE Transactions on Dependable and Secure computing 2025年

作者： Li, Yuqing Yan, Nan Chen, Jing Wang, Xiong Hong, Jianan He, Kun Wang, Wei Li, Bo Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan430072 China Wuhan University RiZhao Information Technology Institute Rizhao276800 China Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan430074 China Shanghai Jiao Tong University School of Cyber Science and Engineering Shanghai200240 China Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

Cross-silo federated learning (FL) enables multiple institutions (clients) to collaboratively build a global model without sharing private data. To prevent privacy leakage during aggregation, homomorphic encryption (HE) is widely used to encrypt model updates, yet incurs high computation and communication overheads. To reduce these overheads, packed HE (PHE) has been proposed to encrypt multiple plaintexts into a single ciphertext. However, the original design of PHE assumes all clients share a single private key, making the system vulnerable to security threats of ciphertexts being intercepted and decrypted by honest-but-curious clients. Also, it does not consider the heterogeneity among different clients, resulting in undermined training efficiency with slow convergence and stragglers. To address these challenges, we propose FedPHE, a secure and efficient FL framework with PHE by jointly exploiting contribution-aware secure aggregation and straggler-resistant client selection. Using CKKS with sparsification and blinding, FedPHE achieves efficient secure aggregation that allows clients to only provide obscured encrypted updates while the server can perform aggregation by accounting for contributions of local updates. To mitigate the straggler effect, we devise a perturbed sketch-based selection to cherry-pick representative clients with heterogeneous models and computing capabilities in a communication-efficient and privacy-preserving manner. We show, through rigorous security analysis and extensive experiments, that FedPHE can efficiently safeguard clients' privacy, achieve 2.45-6.56× training speedup, cut the communication overhead by 1.32-24.85×, and reduce straggler effects by 1.89-2.78×. © 2004-2012 IEEE.

关键词： Privacy by design

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The unintended consequences of retail investor attention: the case of the long-term use of short-term debt

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Finance research Letters 2025年 82卷

作者： Cai Liu Xiaoxiao He Faculty of Finance City University of Macau 402 Stanley Ho Building Avenida Padre Tomás Pereira Taipa Macao China School of Finance Southwestern University of Finance and Economics China Engineering Research Center of Intelligent Finance Ministry of Education China Big Data Laboratory on Financial Security and Behavior SWUFE (Laboratory of Philosophy and Social Sciences Ministry of Education) 555 Liutai Avenue Wenjiang District Chengdu Sichuan 611130 China

We explore the impact of retail investor attention (RIA) on the maturity mismatch between firms’ investment and financing. We document a positive and robust association between RIA and the long-term use of short-term debt (LUSD) of listed Chinese firms. The incremental impact of RIA is more pronounced for firms in more competitive industries, firms with high managerial myopia, and firms with low refinancing risk. Collectively, our findings suggest that RIA encourages managerial opportunistic behavior.

关键词： Debt financing Maturity mismatch Retail investor attention

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Distributed cooperative guidance for multi-missile system with time-varying delays and switching topologies

Distributed cooperative guidance for multi-missile system wi...

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第三十九届中国控制会议

作者： Siquan Zhou Jie Ren Yong Heng Xiwang Dong Qingdong Li School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control Laboratory Beihang University Beijing Aerospace System Engineering Research Institute Beijing Institute of Electronic System Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

Distributed cooperative guidance problems for multi-missile system with time-varying delays and switching topologies are investigated. Firstly, considering the complex communication environment, a two-step cooperative guidance strategy is proposed to achieve the simultaneous attack under time-varying delays and switching topologies. In the first step, a distributed cooperative guidance law using neighboring information is designed to coordinate range-to-go and leading angle based on feedback linearization and consensus theory. The second step starts while multi missiles disconnect from each other and hit the target using PNG（Proportional Navigation Guidance）. Then, the algorithm with three steps is presented to select the parameters in the cooperative guidance law. And the stability of the multi-missile system is proven using Lyapunov-Krasovskii theory. Finally, a numerical simulation is given to demonstrate the effectiveness of the proposed cooperative guidance strategy.

关键词： Cooperative guidance Distributed strategy Consensus theory Time-varying delays Switching topologies

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Smoothness sensor: Adaptive smoothness-transition graph convolutions for attributed graph clustering

arXiv

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arXiv 2020年

作者： Ji, Chaojie Chen, Hongwei Wang, Ruxin Cai, Yunpeng Wu, Hongyan Engineering Research Center for Health Big Data Intelligent Analysis Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China University of Chinese Academy of Sciences Beijing China

Clustering techniques attempt to group objects with similar properties into a cluster. Clustering the nodes of an attributed graph, in which each node is associated with a set of feature attributes, has attracted significant attention. Graph convolutional networks (GCNs) represent an effective approach for integrating the two complementary factors of node attributes and structural information for attributed graph clustering. However, oversmoothing of GCNs produces indistinguishable representations of nodes, such that the nodes in a graph tend to be grouped into fewer clusters, and poses a challenge due to the resulting performance drop. In this study, we propose a smoothness sensor for attributed graph clustering based on adaptive smoothness-transition graph convolutions, which senses the smoothness of a graph and adaptively terminates the current convolution once the smoothness is saturated to prevent oversmoothing. Furthermore, as an alternative to graph-level smoothness, a novel fine-gained node-wise level assessment of smoothness is proposed, in which smoothness is computed in accordance with the neighborhood conditions of a given node at a certain order of graph convolution. In addition, a self-supervision criterion is designed considering both the tightness within clusters and the separation between clusters to guide the whole neural network training process. Experiments show that the proposed methods significantly outperform 12 other state-of-the-art baselines in terms of three different metrics across four benchmark datasets. In addition, an extensive study reveals the reasons for their effectiveness and efficiency. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolution

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Protection of Large Sparse Network Through Optimal Cropped Depth-First Traversal Tree

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IEEE Transactions on Network Science and Engineering 2025年

作者： Wei, Wei Hu, Qiuyuan Zhang, Qinghui Li, Peng Henan University of Technology Key Laboratory of Grain Information Processing and Control Ministry of Education Zhengzhou450001 China Henan University of Technology Henan Key Laboratory of Grain Storage Information Intelligent Perception and Decision Making Zhengzhou450001 China Henan University of Technology Henan Grain Big Data Analysis and Application Engineering Research Center Zhengzhou450001 China Beijing Institute of Technology No. 5 Zhongguancun South Street Haidian District Beijing100086 China

The robustness of the network is its ability to withstand unexpected failures. There is an urgent need to enhance the robustness of sparse networks as they are prone to breakage. Among the various ways to enhance network robustness measured by the edge connectivity of the entire network, shielding candidate edges is a popular method when redundant edges are not affordable. However, existing tree-cutting favors dense graphs with exponential cropped-tree growth of search space, but the search space only scales linearly in sparse graphs. We explore sparse-graph limitations by extending cropping to maximum depth while maintaining sublinear overhead, leveraging ancestor-node constraints and cropping-chain theorems for optimal efficiency. Compared with the optimal algorithm, the results in representative network types show that the robustness measurement of small-scale graphs can be enhanced to 100%, and the ratio of acceleration can be greater than 105. In large-scale graphs with millions of nodes, robustness can be improved to over 99%, while the time can be as low as tens of seconds. © 2013 IEEE.

关键词： Trees (mathematics)

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Advanced Spin Orbit Torque Magnetic Random Access Memory with Field-Free Switching Schemes (Invited)

Advanced Spin Orbit Torque Magnetic Random Access Memory wit...

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International Conference on Solid-State and Integrated Circuit Technology

作者： Chao Wang Zhaohao Wang Shouzhong Peng Youguang Zhang Weisheng Zhao Fert Beijing Research Institute Beihang University Beijing China School of Electronics and Information Engineering Beihang University Beijing China School of Microelectronics Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China

ISBN: (数字)9781728162355

ISBN: (纸本)9781728162362

Magnetic random access memory (MRAM) is considered a promising candidate for future both standalone and embedded memory technology. The emerging spin orbit torque (SOT) mechanism is expected to alternate the traditional spin transfer torque (STT) writing scheme. However, the need of undesirable magnetic field limits the application of SOT mechanism. In this invited paper, we outline our latest achievements on the field-free SOT-MRAM. First, we proposed a toggle spin torque MRAM (TST-MRAM) by combining STT and SOT. Second, we experimentally demonstrated a voltage-gated SOT mechanism assisted by voltage-controlled magnetic anisotropy (VCMA) and in-plane exchange bias (EB). Finally, we presented a current-path-dependent SOT mechanism aided by uniaxial shape anisotropy.

关键词： Magnetization Magnetic tunneling Torque Strips Spintronics Optical switches Logic gates

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Optimizing biomedical ontology alignment in lexical vector space

Optimizing biomedical ontology alignment in lexical vector s...

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作者： Xue, Xingsi Wu, Xiaojing Fujian Key Lab for Automotive Electronics and Electric Drive Fujian University of Technology Fuzhou Fujian Minhou China College of Information Science and Engineering Fujian University of Technology Fuzhou Fujian Minhou China Intelligent Information Processing Research Center Fujian University of Technology Fuzhou Fujian Minhou China Institute of Artificial Intelligence Fujian University of Technology Fuzhou Fujian Minhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou Fujian Minhou China

Biomedical ontology matching dedicates to find two heterogeneous ontologies' alignment and address their heterogeneity problem. Typically, a biomedical ontology has various biomedical concepts that are described with various labels and datatype property names, which forms a lexical space where each label or datatype property represents one dimension. Therefore, it is an effective way to present two biomedical concepts in a vector space, and use the cosine distance to measure their similarity. In this work, we present two biomedical concepts in a lexical vector space which is constructed with their inner and context concepts' lexical information, and then utilize two vector's cosine distance to measure similarity value. Then, we propose a compact Evolutionary Algorithm (cEA) to find the concept correspondences. The experiment uses Ontology Alignment Evaluation Initiative (OAEI)'s testing cases, and the expeirmental results with Vector space Based Ontology Matcher (VBOM), Genetic Algorithm based Ontology Matcher (GAOM) and OAEI's participants show the effectiveness of our proposal. © 2020 - IOS Press and the authors. All rights reserved.

关键词： Vector spaces

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Field-free Deterministic Magnetization Switching Induced by Interlaced Spin-Orbit Torques

arXiv

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arXiv 2020年

作者： Wang, Min Wang, Zhaohao Wang, Chao Zhao, Weisheng Fert Beijing Research Institute Beihang University Beijing China School of Microelectronics Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China School of Electronics and Information Engineering Beihang University Beijing China

Spin-orbit torque (SOT) based magnetic random access memory (MRAM) is envisioned as an emerging non-volatile memory due to its ultra-high speed and low power consumption. The field-free switching schema in SOT devices is of great interest to both academia and industry. Here we propose a novel field-free deterministic magnetization switching in a regular magnetic tunnel junction (MTJ) by using two currents sequentially passing interlaced paths, with less requirements of manufacturing process or additional physical effects. The switching is bipolar since the final magnetization state depends on the combination of current paths. The functionality and robustness of the proposed schema is validated through both macrospin and micromagnetic simulation. The influences of field-like torque and Dzyaloshinskii-Moriya interaction (DMI) effect are further researched. Our proposed schema shows good scalability and is expected to realize novel digital logic and even computing-in-memory platform. Copyright © 2020, The Authors. All rights reserved.

关键词： Torque

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Chinese visceral adiposity index outperforms other obesity indexes in association with increased overall cancer incidence: findings from prospective MJ cohort study: Epidemiology

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British Journal of Cancer 2025年 1-12页

作者： Wang, Mengying Wen, Chi Pang Pan, Junlong Sun, Gege Chu, David Ta-Wei Tu, Huakang Li, Wenyuan Wu, Xifeng Center of Clinical Big Data and Analytics of School of Public Health and the Second Affiliated Hospital Zhejiang University School of Medicine Zhejiang Hangzhou China Institute of Population Health Sciences National Health Research Institutes Zhunan Taiwan Graduate Institute of Biomedical Sciences College of Medicine China Medical University Taichung Taiwan MJ Health Management Center Taipei Taiwan Zhejiang Key Laboratory of Intelligent Preventive Medicine Zhejiang Hangzhou 310058 China National Institute for Data Science in Health and Medicine Zhejiang University Zhejiang Hangzhou 310058 China School of Medicine and Health Science George Washington University Washington DC United States

Background: We assessed the associations of visceral adiposity indexes such as Chinese Visceral Adiposity Index (CVAI), Visceral Adiposity Index (VAI), Lipid Accumulation Product (LAP), waist circumference (WC), and waist-hip ratio (WHR) with overall and specific cancer incidence in a Chinese population. Methods: 332,297 individuals from the Taiwan MJ cohort (1996–2007) were included. We utilized multivariable Cox proportional hazards models to examine associations of baseline visceral adiposity indexes and cancer incidences. Sex-specific CVAI, VAI, and LAP were calculated, incorporating WC and triglycerides levels. CVAI and VAI also included body mass index and high-density lipoprotein, with CVAI further incorporating age. Results: Higher CVAI was consistently associated with higher overall cancer incidence, with HRs of 1.45 (95% CI: 1.2–1.76) and 2.03 (95% CI: 1.52–2.72) for males and females, respectively, comparing the fifth quintile to the first. The HRs for WC were 1.27 (95% CI: 1.08–1.49) and 1.19 (95% CI: 1.01–1.40) for males and females, WHR was significantly associated with cancer risk in males (HR:1.28;95% CI: 1.13–1.45), and LAP was significantly associated with cancer risk in females (HR: 1.25;95% CI: 1.04–1.5). VAI was not associated with overall cancer incidence. Discussion: CVAI is a superior clinical biomarker for predicting cancer incidence in the Chinese population compared to traditional visceral obesity indices. (Figure presented.) © The Author(s) 2025.

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Graph Polish: A novel graph generation paradigm for molecular optimization

arXiv

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arXiv 2020年

作者： Ji, Chaojie Zheng, Yijia Wang, Ruxin Cai, Yunpeng Wu, Hongyan Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China University of Chinese Academy of Sciences Beijing China

Molecular optimization, which transforms a given input molecule X into another Y with desirable properties, is essential in molecular drug discovery. The traditional translating approaches, generating the molecular graphs from scratch by adding some substructures piece by piece, prone to error because of the large set of candidate substructures in a large number of steps to the final target. In this study, we present a novel molecular optimization paradigm, Graph Polish, which changes molecular optimization from the traditional "two-language translating" task into a "single-language polishing" task. The key to this optimization paradigm is to find an optimization center subject to the conditions that the preserved areas around it ought to be maximized and thereafter the removed and added regions should be minimized. We then propose an effective and efficient learning framework T&S polish to capture the long-term dependencies in the optimization steps. The T component automatically identifies and annotates the optimization centers and the preservation, removal and addition of some parts of the molecule, and the S component learns these behaviors and applies these actions to a new molecule. Furthermore, the proposed paradigm can offer an intuitive interpretation for each molecular optimization result. Experiments with multiple optimization tasks are conducted on four benchmark datasets. The proposed T&S polish approach achieves significant advantage over the five state-of-the-art baseline methods on all the tasks. In addition, extensive studies are conducted to validate the effectiveness, explainability and time saving of the novel optimization paradigm. Copyright © 2020, The Authors. All rights reserved.

关键词： Graph neural networks

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