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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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Advancing Forest Fire Prevention: Deep Reinforcement Learning for Effective Firebreak Placement

arXiv

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arXiv 2024年

作者： Murray, Lucas Castillo, Tatiana Carrasco, Jaime Weintraub, Andrés Weber, Richard de Diego, Isaac Martín González, José Ramón García-Gonzalo, Jordi University of Chile Industrial Engineering Department Santiago Chile Departamento de Industria Facultad de Ingeniería Universidad Tecnológica Metropolitana Santiago Chile Complex Engineering System Institute - ISCI Santiago Chile Data Science Lab. Rey Juan Carlos University Madrid Spain Centre de Ciència i Tecnologia Forestal de Catalunya Solsona Spain

Over the past decades, the increase in both frequency and intensity of large-scale wildfires due to climate change has emerged as a significant natural threat. The pressing need to design resilient landscapes capable of withstanding such disasters has become paramount, requiring the development of advanced decision-support tools. Existing methodologies, including Mixed Integer Programming, Stochastic Optimization, and Network Theory, have proven effective but are hindered by computational demands, limiting their applicability. In response to this challenge, we propose using artificial intelligence techniques, specifically Deep Reinforcement Learning, to address the complex problem of firebreak placement in the landscape. We employ value-function based approaches like Deep Q-Learning, Double Deep Q-Learning, and Dueling Double Deep Q-Learning. Utilizing the Cell2Fire fire spread simulator combined with Convolutional Neural Networks, we have successfully implemented a computational agent capable of learning firebreak locations within a forest environment, achieving good results. Furthermore, we incorporate a pre-training loop, initially teaching our agent to mimic a heuristic-based algorithm and observe that it consistently exceeds the performance of these solutions. Our findings underscore the immense potential of Deep Reinforcement Learning for operational research challenges, especially in fire prevention. Our approach demonstrates convergence with highly favorable results in problem instances as large as 40 × 40 cells, marking a significant milestone in applying Reinforcement Learning to this critical issue. To the best of our knowledge, this study represents a pioneering effort in using Reinforcement Learning to address the aforementioned problem, offering promising perspectives in fire prevention and landscape management. © 2024, CC BY.

关键词： Fires

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A deep learning system for predicting time to progression of diabetic retinopathy

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NATURE MEDICINE 2024年第2期30卷 358-359页

作者： [Anonymous] Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Center for Diabetes Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Department of Ophthalmology Huadong Sanatorium Wuxi China Department of Ophthalmology Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong China Singapore Eye Research Institute Singapore National Eye Centre Singapore Singapore Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong China Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong China State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Department of Ophthalmology Peking Union Medical College Hospital Peking Union Medical College Chinese Academy of Medical Sciences Beijing China Medical Records and Statistics Office Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Geriatrics Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China 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 Tech

We developed and validated a deep learning system (termed DeepDR Plus) in a diverse, multiethnic, multi-country dataset to predict personalized risk and time to progression of diabetic retinopathy. We show that DeepDR Plus can be integrated into the clinical workflow to promote individualized intervention strategies for the management of diabetic retinopathy.

关键词： Diabetes complications Machine learning Predictive markers

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Hierarchical Recording Architecture for Three-Dimensional Magnetic Recording

arXiv

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arXiv 2025年

作者： Jian, Yugen Luo, Ke Chen, Jincai Fong, Xuanyao Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan430074 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore School of Computer of Science and Technology Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China Wuhan430074 China

Three-dimensional magnetic recording (3DMR) is a highly promising approach to achieving ultra-large data storage capacity in hard disk drives. One of the greatest challenges for 3DMR lies in performing sequential and correct writing of bits into the multi-layer recording medium. In this work, we have proposed a hierarchical recording architecture based on layered heat-assisted writing with a multi-head array. The feasibility of the architecture is validated in a dual-layer 3DMR system with FePt-based thin films via micromagnetic simulation. Our results reveal the magnetization reversal mechanism of the grains, ultimately attaining appreciable switching probability and medium signal-to-noise ratio (SNR) for each layer. In particular, an optimal head-to-head distance is identified as the one that maximizes the medium SNR. Optimizing the system’s noise resistance will improve the overall SNR and allow for a smaller optimal head-to-head distance, which can pave the way for scaling 3DMR to more recording layers. Copyright © 2025, The Authors. All rights reserved.

关键词： Hard disk storage

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WiNet: Wavelet-based Incremental Learning for Efficient Medical Image Registration

arXiv

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arXiv 2024年

作者： Cheng, Xinxing Jia, Xi Lu, Wenqi Li, Qiufu Shen, Linlin Krull, Alexander Duan, Jinming School of Computer Science University of Birmingham BirminghamB15 2TT United Kingdom Department of Computing and Mathematics Manchester Metropolitan University ManchesterM15 6BH United Kingdom National Engineering Laboratory for Big Data System Computing Technology Shenzhen University 518060 China

Deep image registration has demonstrated exceptional accuracy and fast inference. Recent advances have adopted either multiple cascades or pyramid architectures to estimate dense deformation fields in a coarse-to-fine manner. However, due to the cascaded nature and repeated composition/warping operations on feature maps, these methods negatively increase memory usage during training and testing. Moreover, such approaches lack explicit constraints on the learning process of small deformations at different scales, thus lacking explainability. In this study, we introduce a model-driven WiNet that incrementally estimates scale-wise wavelet coefficients for the displacement/velocity field across various scales, utilizing the wavelet coefficients derived from the original input image pair. By exploiting the properties of the wavelet transform, these estimated coefficients facilitate the seamless reconstruction of a full-resolution displacement/velocity field via our devised inverse discrete wavelet transform (IDWT) layer. This approach avoids the complexities of cascading networks or composition operations, making our WiNet an explainable and efficient competitor with other coarse-to-fine methods. Extensive experimental results from two 3D datasets show that our WiNet is accurate and GPU efficient. Code is available at https://***/x-xc/WiNet. Copyright © 2024, The Authors. All rights reserved.

关键词： Image registration

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Dynamic Partial Computation Offloading for the Metaverse in In-Network Computing

arXiv

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arXiv 2023年

作者： Aliyu, Ibrahim Oh, Seungmin Ko, Namseok Um, Tai-Won Kim, Jinsul Department of ICT Convergence System Engineering Chonnam National University Gwangju61186 Korea Republic of Telecommunication Media Laboratory Electronics and Telecommunications Research Institute Daejeon34129 Korea Republic of Graduate School of Data Science Chonnam National University Gwangju61186 Korea Republic of

The computing in the network (COIN) paradigm is a promising solution that leverages unused network resources to perform tasks to meet computation-demanding applications, such as the metaverse. In this vein, we consider the partial computation offloading problem in the metaverse for multiple subtasks in a COIN environment to minimize energy consumption and delay while dynamically adjusting the offloading policy based on the changing computational resource status. The problem is NP-hard, and we transform it into two subproblems: the task-splitting problem (TSP) on the user side and the task-offloading problem (TOP) on the COIN side. We model the TSP as an ordinal potential game and propose a decentralized algorithm to obtain its Nash equilibrium (NE). Then, we model the TOP as a Markov decision process and propose the double deep Q-network (DDQN) to solve for the optimal offloading policy. Unlike the conventional DDQN algorithm, where intelligent agents sample offloading decisions randomly within a certain probability, the COIN agent explores the NE of the TSP and the deep neural network. Finally, the simulation results reveal that the proposed model approach allows the COIN agent to update its policies and make more informed decisions, leading to improved performance over time compared to the traditional baseline. Copyright © 2023, The Authors. All rights reserved.

关键词： Computation offloading

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AOCC-FL: Federated Learning with Aligned Overlapping via Calibrated Compensation

AOCC-FL: Federated Learning with Aligned Overlapping via Cal...

引用

IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Haozhao Wang Wenchao Xu Yunfeng Fan Ruixuan Li Pan Zhou School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Department of Computing The Hong Kong Polytechnic University Hong Kong 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

Federated Learning enables collaboratively model training among a number of distributed devices with the coordination of a centralized server, where each device alternatively performs local gradient computation and communication to the server. FL suffers from significant performance degradation due to the excessive communication delay between the server and devices, especially when the network bandwidth of these devices is limited, which is common in edge environments. Existing methods overlap the gradient computation and communication to hide the communication latency to accelerate the FL training. However, the overlapping can also lead to an inevitable gap between the local model in each device and the global model in the server that seriously restricts the convergence rate of learning process. To address this problem, we propose a new overlapping method for FL, AOCC-FL, which aligns the local model with the global model via calibrated compensation such that the communication delay can be hidden without deteriorating the convergence performance. Theoretically, we prove that AOCC-FL admits the same convergence rate as the non-overlapping method. On both simulated and testbed experiments, we show that AOCC-FL achieves a comparable convergence rate relative to the non-overlapping method while outperforming the state-of-the-art overlapping methods.

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Software-Defined Networks in IoT Ecosystems for Renewable Energy Resource Management

Software-Defined Networks in IoT Ecosystems for Renewable En...

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IEEE International Conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Ugochukwu O. Matthew Renata L. Rosa Jazuli S. Kazaure Oluwaseun Jeremiah Adesina Odubola Abel Oluwatimilehin Chukwuka Michael Oforgu Olasubomi Asuni Nwamaka U. Okafor Demostenes Z. Rodriguez Department of Computer Science Federal University of Lavras Minas Gerais Brazil Department of Electrical Engineering Hussaini Adamu Federal Polytechnic Kazaure Nigeria Department of Renewable Energy System De Montfort University United Kingdom Leicester UK Computer Science/Mathematics Obafemi Awolowo University Ile-Ife Osun Nigeria Department of Data Science University of the West England Bristol United Kingdom Electrical Electronics Engineering Northumbria University Newcastle Newcastle Upon Tyne United Kingdom School of Elect. Electronics Engineering University College Dublin Dublin Ireland

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

A novel approach toward modernizing energy infrastructures and improving operational performance in distribution of solar renewable energy in smart grid is through Software-Defined Networking (SDN) technology. In addition, the global solar radiation (GSR) plays a critical role in solar power system design in order to precisely identify where solar renewable energy systems should be installed. The hourly global sun radiation measurements using SDN are required in order to precisely calculate the amount of solar energy generated, amount of energy consumed and the amount of solar energy resources conserved. In order to solve the problem of smart grid energy distribution, this study utilized artificial neural network (ANN) algorithm to build a model that predicts, on a monthly basis, the average daily global sun irradiance on a horizontal region of ground base using meteorological data which include the earth's temperature, relative humidity, wind speed, rainfall and ambient air quality. In this paper, the authors discussed the issues in implementing SDN in the energy distribution, energy demand response, energy optimization, grid resilience and real-time monitoring. By using the proposed model, the results showed a good agreement between the measured and calculated levels of global solar irradiation, demonstrating the superiority of the ANN model over empirical models due to its small noise margin. For researchers, practitioners, and policymakers looking to fully leverage SDN for advancing solar renewable infrastructures within national grid system, this research paper offers insightful information through a thorough analysis of current developments and future trends.

关键词： Temperature measurement Renewable energy sources Terrestrial atmosphere Artificial neural networks Solar energy Smart grids Solar radiation Sun Software defined networking Resilience

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Digital Twins in the IIoT: Current Practices and Future Directions Toward Industry 5.0

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Computers, Materials and Continua 2025年第3期83卷 3675-3712页

作者： Bisni Fahad Mon Mohammad Hayajneh Najah Abu Ali Farman Ullah Hikmat Ullah Shayma Alkobaisi Computer & Network Engineering United Arab Emirates University Al Ain 15551 United Arab Emirates Big Data Analytics Centre United Arab Emirates University Al Ain 15551 United Arab Emirates Department of Business Information Technology Liwa College Abu Dhabi 41009 United Arab Emirates Information System & Security United Arab Emirates University Al Ain 15551 United Arab Emirates

In this paper, we explore the ever-changing field of Digital Twins (DT) in the Industrial Internet of Things (IIoT) context, emphasizing their critical role in advancing Industry 4.0 toward the frontiers of Industry 5.0. The article explores the applications of DT in several industrial sectors and their smooth integration into the IIoT, focusing on the fundamentals of digital twins and emphasizing the importance of virtual-real integration. It discusses the emergence of DT, contextualizing its evolution within the framework of IIoT. The study categorizes the different types of DT, including prototypes and instances, and provides an in-depth analysis of the enabling technologies such as IoT, Artificial Intelligence (AI), Extended Reality (XR), cloud computing, and the Application Programming Interface (API). The paper demonstrates the DT advantages through the practical integration of real-world case studies, which highlights the technology’s exceptional capacity to improve traceability and fault detection within the context of the IIoT. This paper offers a focused, application-driven perspective on DTs in IIoT, specifically highlighting their role in key production phases such as designing, intelligent manufacturing, maintenance, resource management, automation, security, and safety. By emphasizing their potential to support human-centric, sustainable advancements in Industry 5.0, this study distinguishes itself from existing literature. It provides valuable insights that connect theoretical advancements with practical implementation, making it a crucial resource for researchers, practitioners, and industry professionals.

关键词： Digital twin in industry integration of DT in IIoT artificial intelligence AI-Driven DT applications

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Remote Observations in China’s Ramsar Sites: Wetland Dynamics, Anthropogenic Threats, and Implications for Sustainable Development Goals

引用

Journal of Remote Sensing 2021年第1期2021卷 319-331页

作者： D.Mao Z.Wang Y.Wang C.-Y.Choi M.Jia M.V.Jackson R.A.Fuller Key Laboratory of Wetland Ecology and Environment Northeast Institute of Geography and AgroecologyChinese Academy of SciencesChangchun 130102China National Earth System Science Data Center BeijingChina Department of Natural Resources Sciences University of Rhode IslandKingstonRI 02881USA School of Environmental Science and Engineering College of EngineeringSouthern University of Science and TechnologyShenzhen 518055China School of Biological Sciences The University of QueenslandBrisbaneAustralia

The Ramsar Convention on Wetlands is an international framework through which countries identify and protect important *** Ramsar wetlands are under substantial anthropogenic pressure worldwide,and tracking ecological change relies on multitemporal data ***,we evaluated the spatial extent,temporal change,and anthropogenic threat to Ramsar wetlands at a national scale across China to determine whether their management is currently *** analyzed Landsat data to examine wetland dynamics and anthropogenic threats at the 57 Ramsar wetlands in China between 1980 and *** reveal that Ramsar sites play important roles in preventing wetland loss compared to the dramatic decline of wetlands in the surrounding ***,there are declines in wetland area at 18 Ramsar *** those,six lost a wetland area greater than 100 km^(2),primarily caused by agricultural *** expansion of anthropogenic land covers occurred within 43(75%)Ramsar sites,and anthropogenic threats from land cover change were particularly notable in eastern *** pond expansion and Spartina alterniflora invasion were prominent threats to coastal Ramsar *** observations within China’s Ramsar sites,which in management regulations have higher levels of protection than other wetlands,can help track progress towards achieving United Nations Sustainable Development Goals(SDGs).The study findings suggest that further and timely actions are required to control the loss and degradation of wetland ecosystems.

关键词： Sustainable threat wetland

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