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Robust and Secure Federated Learning with Verifiable Differential Privacy

IEEE Transactions on Dependable and Secure Computing 2025年

作者： Zhang, Chushan Weng, Jian Weng, Jiasi Zhong, Yijian Liu, Jia-Nan Deng, Cunle College of Cyber Security of Jinan University Guangzhou510632 China College of Cyber Security of Jinan University National Joint Engineering Research Center of Network Security Detection and Protection Technology Guangdong Key Laboratory of Data Security and Privacy Preserving Guangdong-Hong Kong-Macao Joint Laboratory of Data Security and Privacy Preserving Guangzhou510632 China Dongguan University of Technology School of Computer Science and Technology Dongguan523808 China

Federated Learning (FL) has emerged as a popular paradigm for training machine learning models on data from multiple sources without the need for data to leave their respective locations. Although FL is widely used, recent research indicates its vulnerability to data privacy breaches and Byzantine attacks. Addressing both threats simultaneously remains challenging, as previous defense mechanisms struggle to balance Byzantine resilience, data privacy, and training accuracy. Although combining Differential Privacy (DP) with Byzantineresilience in FL offers potential solutions, it presents challenges due to theoretical incompatibilities that can impact the performance of large-scale models. Addressing this underexplored challenge, we propose a novel approach that enhances privacy and Byzantine-resilient in FL while maintaining training accuracy. Our method integrates DP with Byzantine-resilience techniques, overcoming the limitations of existing methods that often sacrifice accuracy or even Byzantine resistance. Our experiments carefully analyze the impact of DP noise on training accuracy and Byzantine-resilience, guiding appropriate parameter selection. The experimental results demonstrate that our approach achieves superior global model accuracy compared to previous competitive methods, even with strong privacy constraints. © 2004-2012 IEEE.

关键词： Federated learning

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IoT Device Recognition Framework Based on network Protocol keyword Query 6th

IoT Device Recognition Framework Based on Network Protocol K...

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6th International Conference on Artificial Intelligence and Security, ICAIS 2020

作者： Xu, Zi-Xiao Dai, Qin-yun Xu, Gang Huang, He Chen, Xiu-Bo Yang, Yi-Xian Information Security Center State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China School of Information Science and Technology North China University of Technology Beijing100144 China Shanghai Branch Coordination Center of China National Computer Network Emergency Response Technical Team Shanghai201315 China State Key Laboratory of Public Big Data Guizhou University GuiyangGuizhou550025 China

ISBN: (纸本)9783030578831

The boost of 5G technology has brought IoT with enhanced feasibility, it is predictable to have a dramatically increased number of the IoT devices with Internet access. However, there will be certain numbers of devices that are extremely vulnerable, particularly with low level of security coding built in. Hackers might have a greater chance to launch DDoS attack. Therefore, identifiable IoT devices linked to the network security vulnerability database might mitigate the risks contributing to a safer cyberspace. This is of great significance to the security of the entire cyberspace. This paper proposes an IoT device identification technology based on network protocol keyword query. Firstly, the traffic packets sent by the IoT device are collected in real time. Then the traffic data carried by different network protocols is extracted by parsing traffic packets. Secondly, the traffic data is filtered and converted into identifiable information related to the identification attribute of the IoT device. Finally, with the search engine of the major Internet of Things e-commerce website, the information about the IoT device is obtained by inputting the protocol keyword. © 2020, Springer Nature Switzerland AG.

关键词： Internet of things

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Towards Benchmarking and Assessing the Safety and Robustness of Autonomous Driving on Safety-critical Scenarios

arXiv

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arXiv 2025年

作者： Li, Jingzheng Liu, Xianglong Wei, Shikui Chen, Zhijun Li, Bing Guo, Qing Yang, Xianqi Pu, Yanjun Wang, Jiakai Zhongguancun Laboratory Beijing100094 China State Key Laboratory of Software Development Environment Beihang University Beijing100191 China Institute of Data Space Hefei Comprehensive National Science Center Hefei China Institute of Information Science Beijing Jiaotong University Beijing100044 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China Beihang University Beijing100091 China A*STAR 138632 Singapore

Autonomous driving has made significant progress in both academia and industry, including performance improvements in perception task and the development of end-to-end autonomous driving systems. However, the safety and robustness assessment of autonomous driving has not received sufficient attention. Current evaluations of autonomous driving are typically conducted in natural driving scenarios. However, many accidents often occur in edge cases, also known as safety-critical scenarios. These safety-critical scenarios are difficult to collect, and there is currently no clear definition of what constitutes a safety-critical scenario. In this work, we explore the safety and robustness of autonomous driving in safety-critical scenarios. First, we provide a definition of safety-critical scenarios, including static traffic scenarios such as adversarial attack scenarios and natural distribution shifts, as well as dynamic traffic scenarios such as accident scenarios. Then, we develop an autonomous driving test framework to comprehensively evaluate autonomous driving systems, encompassing not only the assessment of perception modules but also system-level evaluations. Our work systematically constructs a safety verification process for autonomous driving, providing technical support for the industry to establish standardized test framework and reduce risks in real-world road deployment. Copyright © 2025, The Authors. All rights reserved.

关键词： Highway accidents

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Climatic and Biotic Controls of Evapotranspiration Across Grassland Ecosystems on Tibetan Plateau

SSRN

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SSRN 2024年

作者： Peng, Siyuan Yang, Yue Luo, Dengnan Zeng, Xiang Liang, Minqi Tao, Long Zhang, Guangru Li, Pan Liao, Weijie Guo, Qun Cao, Ruochen Li, Yuzhe Zhang, Weirong Hu, Zhongmin School of Geography South China Normal University Shipai Campus Guangzhou510631 China Hainan Baoting Tropical Rainforest Ecosystem Observation and Research Station Key Laboratory of Agro-Forestry Environmental Processes and Ecological Regulation School of Ecology and Environment Hainan University Haikou570228 China Center for Eco-Environment Restoration Engineering of Hainan Province Hainan University Haikou570228 China Facility of Geographical Science Beijing Normal University Beijing100875 China Zhuhai Branch of State Key Laboratory of Earth Surface Processes and Resource Ecology Beijing Normal University Zhuhai519087 China School of Atmospheric Sciences Guangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle Sun Yat-sen University Guangdong Zhuhai510245 China Key Lab of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China College of Resources and Environment University of Chinese Academy of Sciences Beijing101408 China International Institute for Earth System Sciences Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing University Jiangsu210023 China Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources School of Geography and Ocean Science Nanjing University Jiangsu210023 China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China

Understanding the spatial variations of evapotranspiration (ET) and its components on Tibetan Plateau (TP) is pivotal for grasping the regional water cycle and energy balance dynamics. Yet it is unclear how the changes of the intricate synergy among diverse environment factors significantly impacts the patterns of ET and its transformation state. Addressing this gap, this study employed observational data from 14 sites, utilizing the Shuttleworth-Wallace-Hu (SWH) model to investigate the spatial relationships between ET, its components, and influencing factors. Model parameters of SWH were optimized by Monte Carlo method, achieving a commendable simulation performance with a mean R2 value of 0.75. Our analysis reveals that in alpine meadow, the ratio of transpiration (T) to ET (T/ET) is generally higher (0.55) compared to alpine steppe (0.39). The T/ET values for hover around or below 0.5, indicating a balanced contribution of various ET components. Spatially, both PPT and LAI have an impact on ET and T/ET. PPT emerges as the primary driver of interannual spatial variations in ET, while LAI plays a crucial role in regulating T/ET. Our results further demonstrate that a transition from water-dominated to energy-dominated systems in TP grassland ecosystem at an approximately aridity index of 0.73. This is noteworthy considering the general application of an aridity index of 0.65 to delineate arid from humid regions. It implies that TP grasslands, within the 0.65-0.73 range, remain primarily water-driven. This insight holds substantial implications for comprehending water-energy dynamics in similar ecosystems, challenging the conventional binary categorization of arid and humid areas. Such findings are of immense significance in enhancing understanding of the intricate interactions of the water cycle between land and the atmosphere. © 2024, The Authors. All rights reserved.

关键词： Water supply

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Climatic and Biotic Controls of Evapotranspiration Across Grassland Ecosystems on Tibetan Plateau

SSRN

引用

SSRN 2024年

作者： Peng, Siyuan Yang, Yue Luo, Dengnan Zeng, Xiang Liang, Minqi Tao, Long Zhang, Guangru Li, Pan Liao, Weijie Guo, Qun Cao, Ruochen Li, Yuzhe Zhang, Weirong Hu, Zhongmin School of Geography South China Normal University Shipai Campus Guangzhou510631 China Hainan Baoting Tropical Rainforest Ecosystem Observation and Research Station School of Ecology Hainan University Haikou570228 China Facility of Geographical Science Beijing Normal University Beijing100875 China Zhuhai Branch State Key Laboratory of Earth Surface Processes and Resource Ecology Beijing Normal University Zhuhai519087 China School of Atmospheric Sciences Guangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle Sun Yat-sen University Guangdong Zhuhai510245 China Key Lab of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China College of Resources and Environment University of Chinese Academy of Sciences Beijing101408 China International Institute for Earth System Sciences Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing University Jiangsu 210023 China Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology Key Laboratory for Land Satellite Remote Sensing Applications Ministry of Natural Resources School of Geography and Ocean Science Nanjing University Jiangsu 210023 China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China

Comprehending the spatial variations of evapotranspiration (ET) and its components on Tibetan Plateau (TP) is crucial for grasping the dynamics of the regional water cycle and energy balance. However, the influence of various environmental factors on ET and its components remains unclear. This study addresses this gap by utilizing observational data from 14 sites and employing the Shuttleworth-Wallace-Hu (SWH) model to examine the spatial relationships between ET, its components, and influencing factors. The SWH model parameters were optimized using the Monte Carlo method, achieving a satisfactory simulation performance with an average R2 value of 0.75. Our analysis indicates that in alpine meadow, the ratio of transpiration to ET is generally higher (0.55) compared to alpine steppe (0.39). In terms of spatial distribution, both precipitation and leaf area index significantly impact ET and transpiration. Precipitation emerges as the primary driver of interannual spatial variations in ET, while leaf area index plays a pivotal role in regulating proportion of transpiration. Furthermore, our results reveal a transition from water-dominated to energy-dominated in TP grassland ecosystem at an aridity index of 0.73. This finding is significant as it challenges the common application of an aridity index of 0.65 to distinguish arid from humid regions. It suggests that TP grassland, within the 0.65-0.73 range, remain primarily water-driven. These findings enhance our comprehension of the intricate interactions within the water cycle. © 2024, The Authors. All rights reserved.

关键词： Evapotranspiration

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Coexistence of insulator-like paramagnon and metallic spin-orbit exciton modes in SrIrO3

arXiv

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arXiv 2024年

作者： Paris, Eugenio Zhang, W. Tseng, Y. Efimenko, A. Sahle, C. Strocov, V.N. Skoropata, E. Rolfs, K. Shang, T. Lyu, J. Pomjakushina, E. Medarde, M. Rønnow, H.M. Normand, B. Radovic, M. Schmitt, T. PSI Center for Photon Sciences Villigen-PSI CH-5232 Switzerland European Synchrotron Radiation Facility 71 Avenue des Martyrs Grenoble38043 France PSI Center for Neutron and Muon Sciences Villigen-PSI CH-5232 Switzerland School of Physics and Electronic Science East China Normal University Shanghai200241 China CAS Key Laboratory of Magnetic Materials and Devices Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China LausanneCH-1015 Switzerland PSI Center for Scientific Computing Theory and Data Villigen-PSI CH-5232 Switzerland

We probe the spectrum of elementary excitations in SrIrO3 by using heterostructured [(SrIrO3)m/(SrTiO3)l] samples to approach the bulk limit. Our resonant inelastic x-ray scattering (RIXS) measurements at the Ir L3-edge reveal a robust low-lying collective magnetic mode with an antiferromagnetic (AF) dispersion similar to the insulators Sr2IrO4 and Sr3Ir2O7, albeit with a large gap and much larger linewidth. At higher energies we find the spin-orbit exciton, also strongly broadened, but with an inverted dispersion and doubled periodicity that are controlled by the charge hopping. These results demonstrate that the AF paramagnon persists, somewhat counterintuitively, far into the metallic regime of the insulator-metal transition driven by the degree of confinement in the heterostructure. We conclude that these two excitations, which are contrasting but coexisting hallmarks of strong AF pseudospin and charge fluctuations in a spin-orbit-coupled Mott-Slater material, are properties intrinsic to the ground state of semimetallic perovskite SrIrO3. © 2024, CC BY.

关键词： Perovskite

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Transformation driven visual reasoning

arXiv

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arXiv 2020年

作者： Hong, Xin Lan, Yanyan Pang, Liang Guo, Jiafeng Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Institute for AI Industry Research Tsinghua University Beijing China

This paper defines a new visual reasoning paradigm by introducing an important factor, i.e. transformation. The motivation comes from the fact that most existing visual reasoning tasks, such as CLEVR in VQA, are solely defined to test how well the machine understands the concepts and relations within static settings, like one image. We argue that this kind of state driven visual reasoning approach has limitations in reflecting whether the machine has the ability to infer the dynamics between different states, which has been shown as important as state-level reasoning for human cognition in Piaget’s theory. To tackle this problem, we propose a novel transformation driven visual reasoning task. Given both the initial and final states, the target is to infer the corresponding single-step or multi-step transformation, represented as a triplet (object, attribute, value) or a sequence of triplets, respectively. Following this definition, a new dataset namely TRANCE is constructed on the basis of CLEVR, including three levels of settings, i.e. Basic (single-step transformation), Event (multi-step transformation), and View (multi-step transformation with variant views). Experimental results show that the state-of-the-art visual reasoning models perform well on Basic, but are still far from human-level intelligence on Event and View. We believe the proposed new paradigm will boost the development of machine visual reasoning. More advanced methods and real data need to be investigated in this direction. The resource of TVR is available at https://***/TVR. Copyright © 2020, The Authors. All rights reserved.

关键词： Machine learning

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Comprehensive integrated analysis of MR and DCE-MR radiomics models for prognostic prediction in nasopharyngeal carcinoma

引用

Visual Computing for Industry,Biomedicine,and Art 2023年第1期6卷 329-342页

作者： Hailin Li Weiyuan Huang Siwen Wang Priya SBalasubramanian Gang Wu Mengjie Fang Xuebin Xie Jie Zhang Di Dong Jie Tian Feng Chen Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Medicine and EngineeringBeihang UniversityBeijing 100191China CAS Key Laboratory of Molecular Imaging Institute of AutomationChinese Academy of SciencesBeijing 100190China Department of Radiology Hainan General Hospital(Hainan Affiliated Hospital of Hainan Medical University)HaikouHainan 570311China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Department of Psychiatry Weill Cornell MedicineNew YorkNY 10065USA Department of Radiotherapy Hainan General Hospital(Hainan Affiliated Hospital of Hainan Medical University)HaikouHainan 570311China Department of Radiology Kiang Wu HospitalSanto AntónioMacao 999078China Department of Radiology Zhuhai People’s Hospital(Zhuhai Hospital Affiliated With Jinan University)ZhuhaiGuangdong 519000China Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and TechnologyXidian UniversityXi’anShaanxi 710126China Zhuhai Precision Medical Center Zhuhai People’s HospitalZhuhaiGuangdong 519000China.

Although prognostic prediction of nasopharyngeal carcinoma (NPC) remains a pivotal research area, the role of dynamic contrast-enhanced magnetic resonance (DCE-MR) has been less explored. This study aimed to investigate the role of DCR-MR in predicting progression-free survival (PFS) in patients with NPC using magnetic resonance (MR)- and DCE-MR-based radiomic models. A total of 434 patients with two MR scanning sequences were included. The MR- and DCE-MR-based radiomics models were developed based on 289 patients with only MR scanning sequences and 145 patients with four additional pharmacokinetic parameters (volume fraction of extravascular extracellular space (ve), volume fraction of plasma space (vp), volume transfer constant (Ktrans), and reverse reflux rate constant (kep) of DCE-MR. A combined model integrating MR and DCE-MR was constructed. Utilizing methods such as correlation analysis, least absolute shrinkage and selection operator regression, and multivariate Cox proportional hazards regression, we built the radiomics models. Finally, we calculated the net reclassification index and C-index to evaluate and compare the prognostic performance of the radiomics models. Kaplan-Meier survival curve analysis was performed to investigate the model’s ability to stratify risk in patients with NPC. The integration of MR and DCE-MR radiomic features significantly enhanced prognostic prediction performance compared to MR- and DCE-MR-based models, evidenced by a test set C-index of 0.808 vs 0.729 and 0.731, respectively. The combined radiomics model improved net reclassification by 22.9%-52.6% and could significantly stratify the risk levels of patients with NPC (p = 0.036). Furthermore, the MR-based radiomic feature maps achieved similar results to the DCE-MR pharmacokinetic parameters in terms of reflecting the underlying angiogenesis information in NPC. Compared to conventional MR-based radiomics models, the combined radiomics model integrating MR and DCE-MR showed

关键词： Dynamic contrast-enhanced magnetic resonance imaging Magnetic resonance imaging Radiomics Prognostic prediction

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Climatic and Biotic Controls of Evapotranspiration Across Grassland Ecosystems on the Tibetan Plateau

SSRN

引用

SSRN 2024年

作者： Peng, Siyuan Yang, Yue Luo, Dengnan Zeng, Xiang Liang, Minqi Tao, Long Zhang, Guangru Li, Pan Liao, Weijie Guo, Qun Cao, Ruochen Li, Yuzhe Zhang, Weirong Hu, Zhongmin School of Geography South China Normal University Shipai Campus Guangzhou510631 China Hainan Baoting Tropical Rainforest Ecosystem Observation and Research Station School of Ecology Hainan University Haikou570228 China Facility of Geographical Science Beijing Normal University Beijing100875 China Zhuhai Branch of State Key Laboratory of Earth Surface Processes and Resource Ecology Beijing Normal University Zhuhai519087 China School of Atmospheric Sciences Guangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle Sun Yat-sen University Guangdong Zhuhai510245 China Key Lab of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China College of Resources and Environment University of Chinese Academy of Sciences Beijing101408 China International Institute for Earth System Sciences Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing University Jiangsu210023 China Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources School of Geography and Ocean Science Nanjing University Jiangsu210023 China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China

Study region: The Tibetan Plateau in China, the Third Pole of the *** focus: Accurate descriptions of spatial variation in evapotranspiration (ET) and its components on the Tibetan Plateau (TP) are crucial for understanding the dynamics of the regional water cycle and energy balance. However, how various environmental factors affect ET and its components remains unclear. This study addresses this gap using observational data from 14 sites and employing the Shuttleworth-Wallace-Hu (SWH) model to examine the spatial relationships between ET, its components, and underlying *** hydrological insights for the region: The SWH model parameters were optimized using the Monte Carlo method, achieving a satisfactory model performance with an average R² of 0.75. The ratio of transpiration to ET was generally higher in alpine meadows (0.55) than alpine steppes (0.39). Across the TP, precipitation and the leaf area index together shaped spatial variation in ET and transpiration. Precipitation emerged as the primary driver of interannual variation in ET, while the leaf area index strongly regulated the proportion of ET accounted for by transpiration. Furthermore, above aridity index values of 0.73, grassland ecosystems transitioned from water to energy. This finding is significant as it challenges the common application of an aridity index value of 0.65 to distinguish arid from humid regions. It suggests that TP grasslands, with an AI of 0.65-0.73, remain primarily water-limited. © 2024, The Authors. All rights reserved.

关键词： Biotic

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Design of Target Behavior Intelligent Analysis System Based on Distributed Computing

Design of Target Behavior Intelligent Analysis System Based ...

引用

2019 IEEE International Conference on Signal, Information and data Processing, ICSIDP 2019

作者： Pan, Xinlong Cheng, Xueqi Yao, Libo Zhu, Zhenqiu DIng, Biao Tang, Tiantian Li, Minbo Liu, Chuanhui Institute of Information Fusion Naval Aviation University Yantai China CAS Key Laboratory of Network Data Science Technology Inistitute of Computing Technology Chinese Academy of Sciences Beijing China School of Software Fudan University Shanghai China

ISBN: (纸本)9781728123455

Through the fusion processing of target data obtained by radar, electronic reconnaissance, space reconnaissance, technical reconnaissance and civil cooperative system in early warning and surveillance system, multidimensional target data including explicit state information such as target attributes, type, position, speed and course could be obtained. The latent potential information, such as regular behavior, intention and threat degree, need to be mastered by human intelligence and experience analysis. It is urgent to use big data mining technology to realize intelligent analysis of target behavior. Based on the analysis of big data related technologies, this paper designs an intelligent target behavior analysis system based on distributed computing, which provides theoretical and technical support for intelligent upgrading of intelligence processing systems and command decision systems. © 2019 IEEE.

关键词： data mining

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