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Domain adaptive FL for edge-enabled privacy-preserving MRI analysis

Alexandria engineering Journal 2025年 128卷 324-339页

作者： Saeed Iqbal Imran Arshad Choudhry Inam Ullah Kuljeet Kaur Bong Jun Choi Mohammad Mehedi Hassan College of Mechatronics and Control Engineering Shenzhen University Shenzhen 518060 China Department of Computer Science Faculty of Information Technology & Computer Science University of Central Punjab Lahore Pakistan Department of Computer Engineering Gachon University Seongnam 13120 Republic of Korea Electrical Engineering Department École de technologie supérieure Montréal QC H3C1K3 Canada Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura 140401 Punjab India School of Computer Science and Engineering Soongsil University Seoul Republic of Korea Department of Information Systems College of Computer and Information Sciences King Saud University Riyadh 11543 Saudi Arabia

Data heterogeneity, privacy leakage challenges, the ineffectiveness of conventional collaborative learning techniques, and unresolved managing non-IID data distributions are some of the major obstacles to implementing artificial intelligence (AI) in healthcare. Federated learning (FL) frameworks frequently have trouble distinguishing between privacy protection and model accuracy, especially when used for delicate medical imaging applications. This study presents a novel framework that synergizes federated learning (FL) with edge computing to address these issues while safeguarding patient privacy. Our proposed Domain Adaptive Federated (DAD) learning approach effectively mitigates both inter-client and intra-client data heterogeneity, enabling collaborative model training across diverse medical imaging modalities (MRI, CT, PET) through cross-domain adaptation. Experimental evaluations on MRI brain segmentation datasets demonstrate the superior performance of DAD compared to traditional FL methods, as evidenced by significant improvements in F1-score (96.3), sensitivity (96.0), specificity (97.1), and AUC (96.7). This enhanced accuracy and robustness in handling heterogeneous and privacy-sensitive data render DAD an ideal candidate for privacy-preserving AI in consumer healthcare. By pioneering innovative strategies for collaborative model training and data privacy, this research contributes to the emerging field of edge intelligence, paving the way for improved patient outcomes while adhering to stringent confidentiality and ethical mandates.

关键词： Multi-modal data Domain adaptive learning Federated learning Privacy preservation Domain-specific Data privacy

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Seamless Graph Task Scheduling over Dynamic Vehicular Clouds: A Hybrid Methodology for Integrating Pilot and Instantaneous Decisions

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IEEE Transactions on Services Computing 2025年

作者： Guo, Bingshuo Liwang, Minghui Xia, Xiaoyu Li, Li Jiao, Zhenzhen Hosseinalipour, Seyyedali Wang, Xianbin Xiamen University School of Informatics Fujian China Rmit University School of Computing Technologies MelbourneVIC Australia Tongji University Department of Control Science and Engineering Shanghai China Beijing Teleinfo Technology Co. Ltd. Caict IF-Labs China University at Buffalo-SUNY Department of Electrical Engineering NY United States Western University Department of Electrical and Computer Engineering ON Canada

Vehicular clouds (VCs) play a crucial role in the Internet-of-Vehicles (IoV) ecosystem by securing essential computing resources for a wide range of tasks. This paPertackles the intricacies of resource provisioning in dynamic VCs for computation-intensive tasks, represented by undirected graphs for parallel processing over multiple vehicles. We model the dynamics of VCs by considering multiple factors, including varying communication quality among vehicles, fluctuating computing capabilities of vehicles, uncertain contact duration among vehicles, and dynamic data exchange costs between vehicles. Our primary goal is to obtain feasible assignments between task components and nearby vehicles, called templates, in a timely manner with minimized task completion time and data exchange overhead. To achieve this, we propose a hybrid graph task scheduling (P-HTS) methodology that combines offline and online decision-making modes. For the offline mode, we introduce an approach called risk-aware pilot isomorphic subgraph searching (RA-PilotISS), which predicts feasible solutions for task scheduling in advance based on historical information. Then, for the online mode, we propose time-efficient instantaneous isomorphic subgraph searching (TE-InstaISS), serving as a backup approach for quickly identifying new optimal scheduling template when the one identified by RA-PilotISS becomes invalid due to changing conditions. Through comprehensive experiments, we demonstrate the superiority of our proposed hybrid mechanism compared to state-of-the-art methods in terms of various evaluative metrics, e.g., time efficiency such as the delay caused by seeking for possible templates and task completion time, as well as cost function, upon considering different VC scales and graph task topologies. © 2025 IEEE.

关键词： Undirected graphs

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Evolving Multi-Access Edge Computing (MEC) for Diverse Ubiquitous Resources Utilization: A Survey

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Telecommunication Systems 2025年第2期88卷 1-41页

作者： Khattak, Muhammad Ilyas Yuan, Hui Khan, Ajmal Ahmad, Ayaz Ullah, Inam Ahmed, Manzoor School of Control Science and Engineering Shandong University Shandong Province Jinan China Communication and Information Research Center Sultan Qaboos University Muscat Oman Department of Electrical Engineering College of Engineering King Faisal University Al-Ahsa Saudi Arabia Jinan China School of Computer and Information Science and AI Industrial Technology Research Hubei Engineering University Xiaogan China

The surge in smart device usage and the demand for high-throughput, low-latency mobile networks have accelerated the adoption of distributed smart applications and Collaborative Resource Management (CRM) techniques such as Multi-Access Edge Computing (MEC). With its ability to handle large data volumes, computational demands, scalability, and high energy consumption, MEC represents the next generation of Mobile Cloud Computing (MCC), addressing key challenges in emerging smart applications. MEC supports real-time, high-throughput, and energy-efficient techniques like task offloading and ultra-reliable low-latency communication (URLLC). However, as these techniques become more widespread, their diversity increases, leading to complexities that make them harder to understand and validate in different applications. This includes their use in areas like assistance for unmanned vehicles (UVs), autonomous driving, unmanned aerial vehicles (UAVs), high altitude platforms (HAPs), underwater unmanned vehicles (UUVs), satellite air-ground communication (SAGC), and other emerging systems that rely on Collaborative Resource Management (CRM). Although a number of review articles pertaining to MEC systems exist, we analyzed techniques beyond task offloading that explore MEC system applicability in diverse domains, including UUVs, vehicular edge computing, Re-configurable Intelligent Surfaces (RIS), Massive MIMO, autonomous driving, HAPs, and SAGC. From the perspective of these optimization techniques, this article highlights their role in shaping MEC’s evolution, focusing on parameters such as real-time response, energy efficiency, and other critical factors in task offloading processes. We also explored the role of Standard Development Organizations (SDOs), such as ETSI and IEEE, in advancing MEC systems. This included an analysis of offloading techniques based on their principles, features, and trade-offs, using key performance indicators (KPIs) for fair comparison, while ident

关键词： Computation offloading

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Worst-Case Integrity Attacks and Resilient State Estimation With Partially Secured Measurements

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IEEE Transactions on control of Network Systems 2025年

作者： Zhou, Jing Shang, Jun Chen, Tongwen University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada Tongji University Department of Control Science and Engineering Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai200092 China

This paper examines the problem of optimal deception attacks against state estimation with partially secured measurements, where smart sensors transmit innovation sequences to the remote end for information fusion. Due to resource limitations or defensive countermeasures, the adversary can only modify data packets transmitted through unreliable channels. Meanwhile, the attack should be synthesized with sophistication to deceive an anomaly detector. To investigate the vulnerabilities of such estimation systems without feedback links and enhance security performance, the optimal attack policy is derived by formulating and explicitly solving a convex optimization problem, with the goal of maximizing the sum of estimation errors. Subsequently, a novel attack detection and resilient state estimation algorithm is proposed to ensure an acceptable level of estimation accuracy. The theoretical performance metrics, including false alarm rates for the proposed detector, are provided. Finally, the effectiveness of the results is confirmed through numerical examples. © 2014 IEEE.

关键词： Optimal detection

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Explainable Application Intent for Zero-Touch Networking: An Incorporation of Hypergraph and Transformer

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IEEE Transactions on Communications 2025年

作者： Wu, Bing Zou, Sai Liwang, Minghui Ni, Wei Wang, Xianbin Guizhou University College of Big Data and Information Engineering China National Key Laboratory of Autonomous Intelligent Unmanned Systems Department of Control Science and Engineering China Tongji University Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Shanghai China Data61 CSIRO Australia Department of Electrical and Computer Engineering Western University Canada

The autonomous interpretation of application intent (APPI) represents the primary step towards achieving closed-loop autonomy in zero-touch networking (ZTN) and also a prerequisite for intent-based networking (IBN). However, understanding APPIs and invoking the corresponding network resources require network professionals with extensive technical expertise to customize network service requests (NSRs), which presents significant challenges for the large-scale deployment of ZTN. This paper investigates an interesting problem of autonomous interpretation of APPIs for ZTN, where a novel mechanism integrating hypergraph and transformer with completeness assurance (HyperTrans-CA) is proposed. In particular, we first involve the Bayesian theory to model APPIs interpretability as maximizing the correct transition probability, where hypergraph is used to describe the complex relationship between application characteristics (e.g., scenario function, and performance) and NSRs, including network devices, virtual network functions (VNFs), and resources. Then, the hypergraph is integrated into the encoder, decoder, and attention mechanisms of Transformer, and a completeness assurance mechanism is designed to improve the prediction accuracy. The convergence of HyperTrans-CA and the corresponding convergence speed of the hypergraph-boosted Transformer in the graph search process are also analyzed. Comprehensive simulations and empirical measurements regarding industrial internet demonstrate that HyperTrans-CA can effectively explain/understand APPIs. Compared to the state-of-the-art Transformer and ChatGPT3.5 models, HyperTrans-CA improves the prediction accuracy of APPIs mapped to VNFs by 23% and 46%, respectively, while raising the prediction accuracy of VNF locations by 8.6 and 17.3 times. © 2025 IEEE.

关键词： Bayesian networks

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Convergence of energy-based learning in linear resistive networks

arXiv

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arXiv 2025年

作者： Huijzer, Anne-Men Chaffey, Thomas Besselink, Bart van Waarde, Henk J. Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen Groningen Netherlands CogniGron - Groningen Cognitive Systems and Materials Center Netherlands Control Group Department of Engineering University of Cambridge United Kingdom School of Electrical and Computer Engineering University of Sydney Australia

Energy-based learning algorithms are alternatives to backpropagation and are well-suited to distributed implementations in analog electronic devices. However, a rigorous theory of convergence is lacking. We make a first step in this direction by analysing a particular energy-based learning algorithm, Contrastive Learning, applied to a network of linear adjustable resistors. It is shown that, in this setup, Contrastive Learning is equivalent to projected gradient descent on a convex function, for any step size, giving a guarantee of convergence for the *** Codes 65K10, 68T05, 93B30, 93D99 © 2025, CC BY-SA.

关键词： Contrastive Learning

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Real-Time Inertia Estimation and Validation Using PMU Data and the Swing Equation in Power Systems

Real-Time Inertia Estimation and Validation Using PMU Data a...

引用

IEEE Green Technologies Conference (IEEE-Green)

作者： Mustafa A. Mhawesh Muthanna Al-Sarray Roy McCann Department of Electrical Engineering and Computer Science University of Arkansas Fayetteville AR USA Al-Furat Al-Awsat Technical University Kufa Iraq Operation and Control Office Ministry of Electricity Baghdad Iraq

ISBN: (数字)9798331520182

ISBN: (纸本)9798331520199

This paper presents a method to estimate power system inertia in real-time using Phasor Measurement Unit (PMU) data and the swing equation. Inertia is essential for maintaining power system stability during disturbances. The method calculates both electrical and mechanical power from PMU data and uses this information to estimate inertia by using Matlab. Then it validates the results by comparing calculated values with real measurements of the rate of change of frequency (ROCOF). Further validation is performed using data from a real-world generation trip event. The outcomes show that the proposed method is accurate, with minimal error, making it suitable for real-time applications.

关键词： Accuracy Estimation Power system stability Phasor measurement units Real-time systems Mathematical models Power grids Frequency measurement MATLAB

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Falcon Optimized CNN for the Detection of Alzheimer's Disease in MRI Image

Falcon Optimized CNN for the Detection of Alzheimer's Diseas...

引用

Smart Systems for applications in electrical sciences (ICSSES), International Conference on

作者： S. Febeena Ezhil Jothi L. Mahalakshmi P. Elamurugan Naveenkumar Kaliyan J. Swapna R. Rajeshwari Department of Artificial Intelligence and Data Science Adhi College of Engineering And Technology Kanchipuram Department of Biomedical Engineering Kongunadu College of Engineering and Technology Trichy Department of Instrumentation and Control Engineering Sri Manakula Vinayagar Engineering College Puducherry Department of Computer science and Engineering Veltech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India Department of Electrical and Electronics Engineering J.P. College of Engineering Ayikudi Tenkasi

ISBN: (数字)9798331507671

ISBN: (纸本)9798331507688

Degenerative illnesses that lead to the eventual death of brain neurons afflict a large number of people. Compared to the traditional Machine Learning (ML) approach, the recent success in the medical community of classifying Alzheimer's Disease (AD) utilized a Deep Learning (DL) technique eliminates the necessity for manual feature extraction techniques. Although DL methods are widely used, there is currently a dearth of trustworthy and comprehensive research on the quantitative analysis of brain Magnetic Resonance Image (MRI) data. In this paper, an effective AD classification approach is proposed that use MRI to classify brain structure and diagnose AD. Gaussian filter is used to eliminate noises at different degrees of complexity, noisy backgrounds, low contrast, and partial volume effect in the original input image. Next, K-Means clustering segmentation is useful for dividing image into different regions based on pixel intensity values. Key features for categorization are extracted using the Gray Level Co-Occurrence Matrix (GLCM) method. A Convolutional Neural Network (CNN), enhanced by Falcon Optimization Algorithm (FOA), is employed for AD categorization. The proposed system, implemented on the Python platform using the best Alzheimer's MRI dataset, demonstrates an enhanced accuracy of 93.21 %. Additionally, this model-based approach provides a capable avenue for advancing research in the early detection of AD and holds substantial potential in differentiating AD patients from healthy individuals.

关键词： Training Accuracy Magnetic resonance imaging Neurons Magnetic resonance Feature extraction Convolutional neural networks Alzheimer's disease Optimization Testing

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Multi-Agent Imitation Learning Based Energy Management of a Microgrid With Hybrid Energy Storage and Real-Time Pricing

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IEEE Internet of Things Journal 2025年

作者： Gao, Shuhua Xu, Yizhuo Zhang, Zhaoqian Wang, Zhengfang Zhou, Xiaoyu Wang, Jing Shandong University School of Control Science and Engineering Jinan250100 China The Hong Kong Polytechnic University Department of Electronic and Information Engineering Hong Kong Hong Kong National University of Singapore Department of Electrical and Computer Engineering Singapore117583 Singapore Second Construction Limited Company of China Construction Eighth Engineering Division Jinan Shandong China

Microgrids equipped with hybrid energy storage systems (ESSs) are increasingly critical for balancing the intermittency of renewable energy sources (RESs) and the fluctuations in demand. This paper introduces a novel Multi-Agent Imitation Learning (MAIL) framework for real-time energy management in microgrids, particularly under real-time pricing conditions. The approach leverages a problem decomposition strategy, which separates the energy management task into two phases: first, optimal actions for each ESS are estimated using individual agents, each employing a deep neural network (DNN) trained to emulate an ideal mixed-integer linear programming (MILP) solver;then, a one-step online optimization reacts to these decisions to optimize the remaining system components holistically. Rigorous proofs establish the equivalence of the decomposed subproblems to the original optimization problem, ensuring integrity and effectiveness. Comparative simulation studies with real-world data reveal that our MAIL framework offers significant cost advantages and superior training efficiency relative to both traditional single and emerging multi-agent reinforcement learning (RL) methods. Notably, the operational costs achieved through our approach are markedly lower than those of its counterparts, closely approximating the theoretical minimum. This underscores the method's proficiency and its potential as an effective solution for microgrid energy management. Moreover, the MAIL strategy's multi-agent design allows for straightforward scalability to more extensive systems involving multiple ESSs and shows promising potential for cooperative management across interconnected microgrids. © 2014 IEEE.

关键词： Costs

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Combining machine learning models and rule engines in clinical decision systems: Exploring optimal aggregation methods for vaccine hesitancy prediction

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computers in Biology and Medicine 2025年 188卷 109749-109749页

作者： Kierner, Slawomir Kierner, Piotr Kucharski, Jacek Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Genetics – Blavatnik Institute Sinclair Lab Harvard Medical School D 77 Avenue Louis Pasteur BostonMA02115 United States Faculty of Electrical Electronic Computer and Control Engineering Lodz University of Technology 18/22 Stefanowskiego St. Lodź90-924 Poland

Background: With the increasing application of artificial intelligence (AI) technologies in the healthcare sector and the emergence of new solutions, such as large language models, there is a growing need to combine medical knowledge, often expressed as clinical rules, with advances in machine learning (ML) offering higher prediction accuracy at the expense of decision-making transparency. Purpose: This study investigates the efficacy of various aggregation methods combining the decisions of an AI model and a clinical rule-based (RB) engine in predicting vaccine hesitancy to maximize the effectiveness of patient incentive programs. This is the first study of parallel ensemble of rules and machine learning in clinical context proposing RB confidence-led fusion of ML and RB inference. Methods: A clinical decision system for predicting hesitation to vaccinate is developed based on a differentially private set of longitudinal health records of 974,000 US patients and clinical rules obtained from the present literature. Various approaches based on possibility theory have been explored to maximize classification accuracy, capture and hurdle rates while ensuring trustworthiness in clinical interventions. Results: Our findings reveal that the hybrid approach outperforms the individual models and RB systems when transparency and accuracy are critical. A RB confidence-led approach emerged as the most effective method. The aggregation of mismatched classes relies on RB results when the RB engine has high confidence (expressed as more than the median degree of membership to the vaccination hesitation output function) and on ML predictions when the RB engine exhibits lower confidence. Conclusions: Implementing such an aggregation method preserves the accuracy and capture rates of a clinical decision system, while potentially improving acceptance among healthcare providers. © 2025 The Authors

关键词： Vaccines

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