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Hierarchical Continual Learning for Domain-Knowledge Retention in Healthcare Federated Learning

IEEE Transactions on Consumer Electronics 2025年

作者： Iqbal, Saeed Zhong, Xiaopin Khan, Muhammad Attique Wu, Zongze AlHammadi, Dina Abdulaziz Liu, Weixiang Choudhry, Imran Arshad Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Prince Mohammad Bin Fahd University Department of AI Alkhobar Saudi Arabia Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Information Systems Riyadh11671 Saudi Arabia University of Central Punjab Faculty of Information Technology and Computer Science Department of Software Engineering Lahore Pakistan

Internet of Medical Things (IoMT) applications encounter issues with data protection, continual adaptation, and domain-specific knowledge retention, especially in consumer-centric IoMT scenarios. We overcome these obstacles and facilitate effective knowledge retention and task adaptation in IoMT applications. This study attempts to create a unique privacy-preserving federated learning framework that combines a hierarchical learning structure with Continual Learning (CL). Despite the advancements in Federated Learning (FL), current models have trouble integrating changing datasets in real-time while protecting privacy, as well as catastrophic forgetting, which occurs when previously learned knowledge is lost when adjusting to new tasks. We present a hierarchical learning framework that makes use of three levels of models -Junior Model (JM), Consultant Model (CM), and Senior Consultant Model (SCM) -to overcome these drawbacks. Each level of the model aids in archived retention and domain-knowledge adaptation. To guarantee that the model maintains valuable information over time and adapts to new tasks with ease, our method blends domain adaptation strategies with ongoing learning approaches like knowledge distillation and elastic weight consolidation (EWC). We compare the suggested methodology with current state-of-the-art (SOTA) models on healthcare datasets for tasks like illness diagnosis and medical image categorization. According to our findings, the hierarchical continual learning model performs better than SOTA techniques in terms of accuracy, task adaptability, and privacy protection. In the healthcare industry, our study sets a new standard for privacy-preserving, continuously adaptable federated learning systems, allowing for real-time, scalable IoMT applications that can adapt dynamically to a variety of changing datasets. © 1975-2011 IEEE.

关键词： Patient treatment

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On modal observers for beyond rigid body H∞ control in high-precision mechatronics

arXiv

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arXiv 2022年

作者： Broens, Yorick Butler, Hans Tóth, Roland The Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands ASML Veldhoven Netherlands The Systems and Control Laboratory Institute for Computer Science and Control Hungary

The ever increasing need for performance results in increasingly rigorous demands on throughput and positioning accuracy of high-precision motion systems, which often suffer from position dependent effects that originate from relative actuation and sensing of the moving-body. Due to the highly stiff mechanical design, such systems are typically controlled using rigid body control design approaches. Nonetheless, the presence of position dependent flexible dynamics severely limits attainable position tracking performance. This paper presents two extensions of the conventional rigid body control framework towards active control of position dependent flexible dynamics. Additionally, a novel control design approach is presented, which allows for shaping of the full closed-loop system by means of structured H∞ co-design. The effectiveness of the approach is validated through simulation using a high-fidelity model of a state-of-the-art moving-magnet planar *** Codes 37M15 © 2022, CC BY.

关键词： Closed loop systems

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Eco-driving Incentive Mechanisms for Mitigating Emissions in Urban Transportation

arXiv

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arXiv 2024年

作者： Umar Niazi, M.B. Cho, Jung-Hoon Dahleh, Munther A. Dong, Roy Wu, Cathy Laboratory for Information and Decision Systems Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Division of Decision and Control Systems Digital Futures KTH Royal Institute of Technology StockholmSE-100 44 Sweden Laboratory for Information and Decision Systems Department of Civil and Environmental Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Department of Industrial and Enterprise Systems Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States

This paper proposes incentive mechanisms that promote eco-driving in transportation networks with the overarching objective of minimizing emissions. The transportation system operator provides the drivers with energy-efficient driving guidance throughout their trips, and their eco-driving levels are measured by how closely they follow this guidance via vehicle telematics. Drivers choose their eco-driving levels to optimize a combination of their travel times and their emissions. To obtain optimal budget allocation and recommendations for the incentive mechanism, the system operator gathers drivers’ preferences, or types, to assess each driver’s trip urgency and natural willingness to eco-drive. In a setting where drivers truthfully report their types, we introduce the first-best incentive mechanism and show that the obedience condition holds (i.e., drivers find it optimal to comply with the system operator’s recommendations) when the recommended eco-driving profile constitutes a Nash equilibrium. Moreover, in a setting where drivers can strategically report their types, we introduce the second-best incentive mechanism and show that the proposed mechanism is incentive-compatible (i.e., drivers find it optimal to be truthful). Under this mechanism, we also show that all equilibrium outcomes are at least as good as the recommended eco-driving profile in terms of the system operator’s objective. Overall, this work offers a framework for designing eco-driving incentive mechanisms while considering both the strategic behavior of individual drivers and the network effects of collective decision-making. © 2024, CC BY.

关键词： Budget control

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Abstraction-Based Synthesis of controllers for Approximate Opacity

Abstraction-Based Synthesis of Controllers for Approximate O...

引用

IEEE Conference on Decision and control

作者： Junyao Hou Siyuan Liu Xiang Yin Majid Zamani Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden Department of Computer Science University of Colorado Boulder CO USA Department of Computer Science LMU Munich Munich Germany

Opacity is an important information-flow security property which characterizes the plausible deniability of certain “secret behaviors” in dynamical systems. In this paper, we study the problem of synthesizing controllers enforcing a notion of opacity over discrete-time control systems with continuous state sets. In this paper, we develop an abstraction-based approach to tackle the controller synthesis problem. Specifically, we adopt a notion of approximate opacity which is suitable for continuous-space control systems. We propose a notion of approximate initial-state opacity preserving alternating simulation relation which characterizes the closeness between two systems in terms of opacity preservation. We show that, based on this new notion of system relation, one can synthesize an opacity-enforcing controller for the abstract system which is finite and then refine it back to enforce opacity over the original control system. Finally, we present a method for constructing opacity-preserving finite abstractions for discrete-time control systems under some stability properties. Our results are illustrated on a two-room temperature control problem.

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The Impact of the Digital Transformation of Education on Literacy

The Impact of the Digital Transformation of Education on Lit...

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International IEEE Conference and Workshop: Óbuda on Electrical and Power Engineering (CANDO-EPE)

作者： Imre Farkas Attila Kovari Mónika Rajcsanyi-Molnar Department of Computer Systems and Control Technology Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Institute of Digital Technology Faculty of Computer Science Eszterházy Károly Catholic University Eger Hungary Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemét Hungary Department of Economics and Management Institute of Social Sciences University of Dunaujvaros Dunaujvaros Hungary

ISBN: (数字)9798331531119

ISBN: (纸本)9798331531126

During the COVID19 epidemic, people of all ages from all walks of life around the world have become inevitably familiar with and almost dependent on the digital tools of the age and the opportunities they offer. A change had to take place very quickly that was unprecedented. This process has also affected education. During the time of the reading lock-in, there was a proliferation of digital curricular content. The second time around, online education came to the fore, thanks to less or well-functioning online platforms, and has since become part of everyday teaching. What is the effectiveness and usefulness of sharing knowledge in this way? Is literacy still needed at all? Can online and hybrid (a controlled mix of face-to-face and online) education replace the well-established contact and face-to-face education? The study show that the respondents believe that online education has had an impact on literacy and that it is perceived to have a positive impact. The results indicate that online education has a positive impact on the literacy levels of students, as it facilitates knowledge acquisition and enhances digital literacy skills.

关键词： Power engineering Epidemics Navigation Knowledge acquisition Digital transformation Education Information age Digital intelligence Cultural differences Problem-solving

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Leakage Localization in Water Distribution Networks: A Model-Based Approach

Leakage Localization in Water Distribution Networks: A Model...

引用

European control Conference (ECC)

作者： Ludvig Lindströ m Sebin Gracy Sindri Magnú sson Henrik Sandberg Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology and Digital Futures Stockholm Sweden Department of Computer and Systems Sciences Stockholm University

ISBN: (数字)9783907144077

ISBN: (纸本)9781665497336

The paper studies the problem of leakage localization in water distribution networks. For the case of a single pipe that suffers from a single leak, by taking recourse to pressure and flow measurements, and assuming those are noiseless, we provide a closed-form expression for leak localization, leak exponent and leak constant. For the aforementioned setting, but with noisy pressure and flow measurements, an expression for estimating the location of the leak is provided. Finally, assuming the existence of a single leak, for a network comprising of more than one pipe and assuming that the network has a tree structure, we provide a systematic procedure for determining the leak location, the leak exponent, and the leak constant.

关键词： Location awareness Knowledge engineering Closed-form solutions Systematics Network topology Distribution networks Topology

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The Emergence of Artificial Intelligence in Education and its Impact on Individual Literacy in Higher Education

The Emergence of Artificial Intelligence in Education and it...

引用

International IEEE Conference and Workshop: Óbuda on Electrical and Power Engineering (CANDO-EPE)

作者： Imre Farkas Attila Kovari Mónika Rajcsanyi-Molnar Department of Computer Systems and Control Technology Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Institute of Digital Technology Faculty of Computer Science Eszterházy Károly Catholic University Eger Hungary Institute of Electronics and Communication Systems Kandó Kalmán Faculty of Electrical Engineering Obuda University Budapest Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemét Hungary Department of Economics and Management Institute of Social Sciences University of Dunaujvaros Dunaujvaros Hungary

ISBN: (数字)9798331531119

ISBN: (纸本)9798331531126

In the last three years, a new partner has emerged for teachers and educators in the field of education. The mushrooming of applications based on large language models has greatly shaped the educational development fields of the present era. The use of various AI applications has become commonplace among students and teachers alike. Many questions are being raised by researchers in this field. What does the rapid development of AI applications bring to the field of pedagogy? Should the products of such applications be compared with human performance? The form presented in this thesis seeks to answer similar questions. What is the impact of frequent use of these applications on the literacy level of individuals? In which areas do students tend to use these applications? The results indicate that half of the students believe their literacy levels will decrease with the mass emergence of AI applications, while 36% feel it will not change. However, 88 % of students have already dealt with AI-based applications, with 64 % using them several times or more, suggesting a high level of integration into their educational processes.

关键词： Ethics Power engineering Large language models Education Collaboration Learning (artificial intelligence) Reliability Artificial intelligence Monitoring Guidelines

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Cluster Forming of Multiagent systems in Rolling Horizon Games with Non-uniform Horizons

arXiv

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arXiv 2023年

作者： Nugraha, Yurid Cetinkaya, Ahmet Hayakawa, Tomohisa Ishii, Hideaki Zhu, Quanyan The Department of Systems and Control Engineering Tokyo Institute of Technology Tokyo152-8552 Japan The Shibaura Institute of Technology Tokyo135-8548 Japan The Department of Computer Science Tokyo Insitute of Technology Yokohama226-8502 Japan The Department of Electrical and Computer Engineering New York University BrooklynNY11201 United States

Consensus and cluster forming of multiagent systems in the face of jamming attacks along with reactive recovery actions by a defender are discussed. The attacker is capable to disable some of the edges of the network with the objective to divide the agents into a smaller size of clusters while, in response, the defender recovers some of the edges by increasing the transmission power. We consider repeated games where the resulting optimal strategies for the two players are derived in a rolling horizon fashion. The attacker and the defender possess different computational abilities to calculate their strategies. This aspect is represented by the non-uniform values of the horizon lengths and the game periods. Theoretical and simulation based results demonstrate the effects of the horizon lengths and the game periods on the agents’ states. © 2023, CC BY.

关键词： Multi agent systems

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A survey on secure decentralized optimization and learning

arXiv

引用

arXiv 2024年

作者： Liu, Changxin Bastianello, Nicola Huo, Wei Shi, Yang Johansson, Karl H. Division of Decision and Control Systems KTH Royal Institute of Technology and Digital Futures Stockholm Sweden Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Department of Mechanical Engineering University of Victoria VictoriaBC Canada

Decentralized optimization has become a standard paradigm for solving large-scale decision-making problems and training large machine learning models without centralizing data. However, this paradigm introduces new privacy and security risks, with malicious agents potentially able to infer private data or impair the model accuracy. Over the past decade, significant advancements have been made in developing secure decentralized optimization and learning frameworks and algorithms. This survey provides a comprehensive tutorial on these advancements. We begin with the fundamentals of decentralized optimization and learning, highlighting centralized aggregation and distributed consensus as key modules exposed to security risks in federated and distributed optimization, respectively. Next, we focus on privacy-preserving algorithms, detailing three cryptographic tools and their integration into decentralized optimization and learning systems. Additionally, we examine resilient algorithms, exploring the design and analysis of resilient aggregation and consensus protocols that support these systems. We conclude the survey by discussing current trends and potential future directions. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

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FISS GAN:A Generative Adversarial Network for Foggy Image Semantic Segmentation

引用

IEEE/CAA Journal of Automatica Sinica 2021年第8期8卷 1428-1439页

作者： Kunhua Liu Zihao Ye Hongyan Guo Dongpu Cao Long Chen Fei-Yue Wang School of Computer Science and Engineering Sun Yat-sen UniversityGuangzhou 510006China State Key Laboratory of Automotive Simulation and Control and the Department of Control Science and Engineering Jilin University(Campus Nanling)Changchun 130025China Mechanical and Mechatronics Engineering Department at the University of Waterloo 200 University Avenue West WaterlooON N2L 3G1Canada State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China

Because pixel values of foggy images are irregularly higher than those of images captured in normal weather(clear images),it is difficult to extract and express their *** method has previously been developed to directly explore the relationship between foggy images and semantic segmentation *** investigated this relationship and propose a generative adversarial network(GAN)for foggy image semantic segmentation(FISS GAN),which contains two parts:an edge GAN and a semantic segmentation *** edge GAN is designed to generate edge information from foggy images to provide auxiliary information to the semantic segmentation *** semantic segmentation GAN is designed to extract and express the texture of foggy images and generate semantic segmentation *** on foggy cityscapes datasets and foggy driving datasets indicated that FISS GAN achieved state-of-the-art performance.

关键词： Edge GAN foggy images foggy image semantic segmentation GAN semantic segmentation

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