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Multi-agent Collaboration for Vehicular Task Offloading Using Federated Deep Reinforcement Learning

IEEE Transactions on Mobile Computing 2025年

作者： Chen, Xing Xiao, Bohuai Lin, Xinyu Chen, Zheyi Min, Geyong Fuzhou University College of Computer and Data Science Fuzhou350116 China Ministry of Education Engineering Research Center of Big Data Intelligence Fuzhou350002 China Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350116 China University of Exeter Department of Computer Science ExeterEX4 4QF United Kingdom

Mobile Edge Computing (MEC) distributes resources such as computing, storage, and bandwidth to the side close to users, which can provide low-latency services to in-vehicle users, thus promising a more efficient and safer driving environment. However, due to the dynamic scale of vehicle and the variability of resource requirements, it is a significant challenge to quickly obtain effective task offloading in large-scale vehicle scenarios. The existing studies generally adopt the centralized decision-making method, with long decision-making time and high computational overhead, which cannot effectively achieve good offloading decisions in large-scale scenarios. To address these problems, we propose a Multi-agent Collaborative Method for vehicular task offloading using Federated Deep Reinforcement Learning called MCM-FDRL. First, each vehicle as an agent, independently makes offloading decisions based on local information. Next, the offloading decision model of each vehicle is obtained through federated reinforcement learning training. At runtime, an effective vehicle offloading plan can be gradually developed through multi-agent collaboration. Using two real-world datasets, experiments show that the MCM-FDRL has good adaptability and scalability. Moreover, compared to the state-of-the-art methods, the task's average response time of the MCM-FDRL is reduced by 9.75%-64.90%, respectively. © 2002-2012 IEEE.

关键词： Federated learning

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A Decentralized Identity System for Accelerating Medical Communications within Rare Disease Communities

A Decentralized Identity System for Accelerating Medical Com...

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Blockchain, Smart Healthcare and Emerging Technologies (SmartBlock4Health), IEEE International Conference on

作者： Fateen Anam Rafid Eloise Benissan Lincoln Murr Ilya Ermakov Konstantinos Oikonomou Peng Zhang Department of Computer Science Vanderbilt University Nashville TN USA Department of Computer Science and Data Science Institute Vanderbilt University Nashville TN USA

ISBN: (纸本)9781665460156

This paper presents a blockchain-based architecture for facilitating clinical communications within the historically underfunded rare disease community. Due to the complex nature of diagnosing and treating these conditions, patients with rare diseases are often referred to multiple care providers and specialists. Each care provider instructs patients to provide identification information to onboard them to their in-house medical record system. An effective identity management service or platform is needed to avoid repetitive steps and to identify patients in a scalable, collaborative, and sustainable way. This work proposes a decentralized, blockchain-based identity management infrastructure driven by interoperability across data management systems to accurately identify unique patients within rare disease communities and provide a supporting platform that facilitates the management and exchanges of information about identities. The proposed system identifies and represents participants with specific roles using a domain-specific digital identifier model. Once a patient's information is certified by a verifier, a Unique Digital Identifier is created for them that is linked to a persistent document holding their “cyber” identity. This allows the patient to identify themselves by presenting only the digital proof of their Unique Digital Identifier ownership during the subsequent medical encounters. Overall, we present a conceptual framework that allows for the fast identification of patients in the rare disease community that minimizes duplication of entries. It also enables providers and researchers to receive updates and exchange knowledge about rare diseases in an unambiguous way.

关键词： Collaboration Medical services Blockchains Medical diagnostic imaging Interoperability Diseases

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Joint Class-Balanced Client Selection and Bandwidth Allocation for Cost-Efficient Federated Learning in Mobile Edge Computing Networks

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IEEE Transactions on Mobile Computing 2025年第7期24卷 5681-5698页

作者： Tang, Jian Li, Xiuhua Li, Hui Li, Penghua Wang, Xiaofei Leung, Victor C. M. Chongqing University School of Big Data & Software Engineering Chongqing400000 China Chongqing University of Posts and Telecommunications School of Automation Chongqing400065 China Tianjin University College of Intelligence and Computing Tianjin300072 China Shenzhen MSU-BIT University Artificial Intelligence Research Institute Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T1Z4 Canada

Federated Learning (FL) has significant potential to protect data privacy and mitigate network burden in mobile edge computing (MEC) networks. However, due to the system and data heterogeneity of mobile clients (MCs), client selection and bandwidth allocation is key for achieving cost-efficient FL in MEC networks with limited bandwidth. To address these challenges, we investigate the issue of joint client selection and bandwidth allocation for reducing the cost (i.e., latency and energy consumption) of FL training. We formulate the problem and decompose it into a holistic subproblem to reduce the number of rounds and a partial subproblem to reduce the costs of FL each round. We propose a joint class-balanced client selection and bandwidth allocation (CBCSBA) framework to address the whole problem. Specifically, for the holistic subproblem, CBCSBA combines MCs into groups, each having data distribution as close as possible to class-balanced distribution;For the partial subproblem, CBCSBA reduces costs by exploratively selecting a group and sequentially optimizing the latency and energy consumption of MCs within the group. Experimental results show that CBCSBA outperforms the baseline frameworks in reducing latency by 28.2% and energy consumption by 25.3% on average in the considered four datasets. © 2002-2012 IEEE.

关键词： Federated learning

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From Efficiency to Innovation: LLM 5.0 and the Future of Industry

From Efficiency to Innovation: LLM 5.0 and the Future of Ind...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： Pradosh Kumar Gantayat Tejinder Kaur Madhusmita Majhi Uttam Kumar Jena Swatismita Das Soni Department of Artificial Intelligence and Data Science Faculty of Science and Technology (IcfaiTech) The ICFAI Foundation for Higher Education Hyderabad Telangana INDIA Department of MMICTBM Maharishi Markandeshwar (Deemed To Be University) Mullana Ambala Haryana India Department of Computer Science and Engineering Faculty of Science and Technology (IcfaiTech) The ICFAI Foundation for Higher Education Hyderabad Telangana India Department of Computer Science and Engineering Raajdhani Engineering College Bhubaneswar Biju Patnaik University of Technology Odisha Raajdhani Engineering College Bhubaneswar

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Industry 5.0 is a new way of thinking that is consistent with the ideas of Industry 4.0 but places greater emphasis on sustainability, sustainability, and human-centricity. Unlike Industry 4.0, which emphasizes the efficient integration of advanced technologies such as the Internet, AI, robots, and automation, Industry 5.0 seeks to balance human-machine collaboration to create more inclusive, meaningful, and sustainable systems. In Industry 5.0, LLM has a bigger role than automating similar activities. LLMs make sectors more resilient, adaptable and aligned with social values by fostering innovation, improving human-centred processes and promoting sustainable practices. Industry 5.0 will create a revolutionary force in the global industrial landscape thanks to a combination of emerging technologies that will further enhance the synergies between human creativity and machine intelligence.

关键词： Industries Technological innovation Service robots Human-machine systems Collaboration Fourth Industrial Revolution Internet Fifth Industrial Revolution Sustainable development Machine intelligence

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Deep Residual W-Unit Learning with Semantic Embedding for Automatic Pulmonary CT Artery-Vein Separation

Deep Residual W-Unit Learning with Semantic Embedding for Au...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Hao Qi Ming Wu Sunkui Ke Xiangxing Chen Hui-Qing Zeng Yinran Chen Xiongbiao Luo Department of Computer Science and Technology Xiamen University Xiamen China Zhongshan Hospital Xiamen University Xiamen China National Institute for Data Science in Health and Medicine Xiamen University Xiamen China

Automatic segmentation of pulmonary arteries and veins in CT has great clinical significance. Because the growth range of a single vessel is vast, and the arteries and veins have barely identical intensity values on CT and grow very close to or even interleaved, accurate segmentation of them requires intricate vascular texture information and long-distance vascular trunk information as the basis for artery and vein classification. In order to meet these two requirements simultaneously, we design a residual W-Unit, which concatenated two U-shaped structures. It allows the network to become deeper and improve the receptive field for global information while preserving the detailed features of the vessels. And we design a semantic embedding module using cross-attention, which enhances the expression of bronchial features and assists in further utilizing features. It explicitly leverages the anatomical knowledge of parallel growth between arteries and bronchi. Then we combine RWUs and SEMs to construct a concise network to extract and fuse the features with detailed information from different network depths and receptive fields. Finally, we use a post-processing scheme to reduce spatial inconsistency. We validated our networks on 40 training sets and 17 test sets, and the experimental results show that our networks outperform current segmentation methods.

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Diabetic Retinopathy Imaging: Network Exudate Detection to Classify Abnormalities

Diabetic Retinopathy Imaging: Network Exudate Detection to C...

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Intelligent Computing and Control for Engineering and Business Systems (ICCEBS)

作者： A. Chinnasamy P. Selvakumari Department of Data Science and Business Systems school of computing SRMIST kattankulathur campus Chennai Department of Computer Science and Engineering Chennai Institute of Technology Chennai Anna University

The DR becomes increasingly common, there is a need to automatically extract and classify disease severity. Due to diabetes problems, about 2% of people with this disease become completely blind, and DR complications make him 10% visually impaired if he has diabetes for 15 years. Furthermore, it plays an important role in the progression of blindness in middle-aged and older adults. If the disease is not recognized as soon as possible, patients can experience a severe stage of irreversible blindness. The shortage of ophthalmologists is a serious problem for the growing number of diabetic patients. Hard exudates must be found to screen and assist in disease monitoring and diagnosis. Therefore, Lloyd's clustering technique was used in this work.

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Some Results on Critical (P5,H)-free Graphs

arXiv

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arXiv 2024年

作者： Xia, Wen Jooken, Jorik Goedgebeur, Jan Huang, Shenwei College of Computer Science Nankai University Tianjin300071 China Tianjin Key Laboratory of Network and Data Security Technology Nankai University Tianjin300071 China Department of Computer Science KU Leuven Campus Kulak-Kortrijk Kortrijk8500 Belgium Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent9000 Belgium

Given two graphs H1 and H2, a graph is (H1,H2)-free if it contains no induced subgraph isomorphic to H1 nor H2. A graph G is k-vertex- critical if every proper induced subgraph of G has chromatic number less than k, but G has chromatic number k. The study of k-vertex- critical graphs for specific graph classes is an important topic in algo- rithmic graph theory because if the number of such graphs that are in a given hereditary graph class is finite, then there exists a polynomial- time certifying algorithm to decide the k-colorability of a graph in the class. In this paper, we show that: (1) for k ≥ 1, there are finitely many k-vertex-critical (P5,K1,4 + P1)-free graphs;(2) for s ≥ 1, there are finitely many 5-vertex-critical (P5,K1,s + P1)-free graphs;(3) for k ≥ 1, there are finitely many k-vertex-critical (P5,K3 + 2P1)-free graphs. Moreover, we characterize all 5-vertex-critical (P5,H)-free graphs where H ∈ {K1,3+P1,K1,4+P1,K3 + 2P1} using an exhaustive graph generation algorithm. © 2024, CC BY.

关键词： Graphic methods

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Time-Slotted On-Demand Predictive Routing for UAV Networks

Time-Slotted On-Demand Predictive Routing for UAV Networks

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Feng, Houze Wang, Jingjing Chen, Jianrui Hou, Xiangwang Wang, Jiacheng Sun, Geng Niyato, Dusit School of Cyber Science and Technology Beihang University Beijing China Tsinghua University Department of Electronic Engineering Beijing China College of Computing and Data Science Nanyang Technological University Singapore College of Computer Science and Technology Jilin University Changchun China

ISBN: (纸本)9798350368369

Flying ad hoc networks (FANETs) composed of small unmanned aerial vehicles (UAVs) are flexible, inexpensive, and fast to deploy, which have been used in an increasing number of mission scenarios. However, unstable link quality and frequently changing network topology pose significant challenges for adopting existing routing protocols in mobile ad hoc networks (MANETs). In this paper, we propose a time-slotted on-demand predictive (TSDP) routing protocol designed specifically for UAV networks. The TSDP protocol introduces a novel approach to route selection by incorporating multiple criteria, including delivery ratio, adjacent degree, and mobility prediction factor, to ensure reliable and efficient data transmission. By addressing high latency in route discovery and excessive broadcast overhead, TSDP employs a time-slotted communication mechanism that reduces packet drop rates and enhances route stability. Simulation results demonstrate that TSDP consistently outperforms ad hoc on-demand distance vector (AODV) and dynamic source routing (DSR) protocols in terms of throughput, packet delivery ratio, end-to-end delay, and overhead, particularly in highly dynamic network environments. © 2025 IEEE.

关键词： Drones

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Explainable machine learning for sleep apnea prediction

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Procedia computer science 2022年 207卷 2930-2939页

作者： A.R. Troncoso-García M. Martínez-Ballesteros F. Martínez-Álvarez A. Troncoso Data Science and Big Data Lab Pablo de Olavide University 41013 ES-Seville Spain Department of Computer Science University of Seville 41012 ES-Seville Spain

Machine and deep learning has become one of the most useful tools in the last years as a diagnosis-decision-support tool in the health area. However, it is widely known that artificial intelligence models are considered a black box and most experts experience difficulties explaining and interpreting the models and their results. In this context, explainable artificial intelligence is emerging with the aim of providing black-box models with sufficient interpretability so that models can be easily understood and further applied. Obstructive sleep apnea is a common chronic respiratory disease related to sleep. Its diagnosis nowadays is done by processing different data signals, such as electrocardiogram or respiratory rate. The waveform of the respiratory signal is of importance too. Machine learning models could be applied to the signal's analysis. data from a polysomnography study for automatic sleep apnea detection have been used to evaluate the use of the Local Interpretable Model-Agnostic (LIME) library for explaining the health data models. Results obtained help to understand how several features have been used in the model and their influence in the quality of sleep.

关键词： Explainable artificial intelligence health data polysomnography LIME

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Hybrid System with Modified Cuckoo Search to Design Airfoil

Hybrid System with Modified Cuckoo Search to Design Airfoil

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data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： K. Thinakaran M. Anand T.Rajesh Kumar Saira Banu Atham K.M. Subramanian Department of Computer Science Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chennai Saveetha School of Engineering Saveetha Institute of Medical and Technical Science Department of Computer Science and Engineering School of CSE & IS Presidency University Bangaluru Department of Computer Science Engineering Shadan College of Engineering and Technology Hyderabad

The proposed models can design the airfoil by Cuckoo search with Levenberg-Marquardt. The Neural Network framework has impediments due to over-fitting. This paper proposed a modified cuckoo search. here the aerodynamic coefficient as an input to produce output the airfoil coordinates. The generated airfoil is compared to know its performance metrics. The Cuckoo search with the Feedforward Neural Network model yields the lowest prediction error.

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