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A Revolution of Personalized Healthcare: Enabling Human Digital Twin With Mobile AIGC

IEEE NETWORK 2024年第6期38卷 234-242页

作者： Chen, Jiayuan Yi, Changyan Du, Hongyang Niyato, Dusit Kang, Jiawen Cai, Jun Shen, Xuemin Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 210016 Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Concordia Univ Dept Elect & Comp Engn Montreal PQ H3G 1M8 Canada Univ Waterloo Dept Elect & Comp Engn Waterloo ON Canada

Mobile artificial intelligence-generated content (AIGC) refers to the adoption of generative artificial intelligence (GAI) algorithms deployed at mobile edge networks to automate the information creation process while fulfilling the requirements of end users. Mobile AIGC has recently attracted phenomenal attentions and can be a key enabling technology for an emerging application, called human digital twin (HDT). HDT empowered by the mobile AIGC is expected to revolutionize the personalized healthcare by generating rare disease data, modeling highfidelity digital twin, building versatile testbeds, and providing 24/7 customized medical services. To promote the development of this new breed of paradigm, in this article, we propose a system architecture of mobile AIGC-driven HDT and highlight the corresponding design requirements and challenges. Moreover, we illustrate two use cases, i.e., mobile AIGC-driven HDT in customized surgery planning and personalized medication. In addition, we conduct an experimental study to prove the effectiveness of the proposed mobile AIGC-driven HDT solution, which shows a particular application in a virtual physical therapy teaching platform. Finally, we conclude this article by briefly discussing several open issues and future directions.

关键词： Medical services Data models Brain modeling Solid modeling Digital twins Data collection distributed databases

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Data Integrity Verification in Mobile Edge Computing With Multi-Vendor and Multi-Server

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第5期23卷 5418-5432页

作者： Zhao, Yao Qu, Youyang Chen, Feifei Xiang, Yong Gao, Longxiang Deakin Univ Sch Informat Technol Burwood VIC 3125 Australia Qilu Univ Technol Shandong Acad Sci Shandong Comp Sci Ctr Shandong Fundamental Res Ctr Comp SciKey Lab Comp Jinan 250316 Peoples R China

The emerging Mobile Edge Computing (MEC) paradigm reforms the way of data caching by motivating App vendors to store latency-sensitive data on distributed edge servers. In volatile MEC environments, ensuring Edge Data Integrity (EDI) is a major concern for App vendors. Existing EDI solutions only consider the scenario with a single App vendor and multiple edge servers, neglecting more complex multi-vendor and multi-server cases. If multiple App vendors check their data replicas cached on the same edge server simultaneously, integrity verification efficiency will drop exponentially. To mitigate this challenge, we make the first attempt to develop a Smart Inspection Algorithm (SIA) to pre-select unreliable data replicas for different App vendors in each verification round by jointly considering cache services' QoS (Quality-of-Service) and data replicas' unverified time. By implementing this approach, edge servers can merely verify the selected data replicas, greatly reducing computation and communication overheads in EDI verification. Theoretically, SIA can achieve O(n) expected time complexity. Supported by SIA, we expand the EDI problem in multi-vendor and multi-server MEC environments (referred to as the MVMS-EDI problem) and propose a smart contract-based approach entitled MVMS-SC to tackle the problem efficiently and impartially. We provide a rigorous theoretical analysis of the correctness, security, and efficiency of MVMS-SC. Both large-scale and small-scale experiments with real-world datasets are correspondingly performed on a single machine and a real platform to validate the superiority of MVMS-SC in terms of computation and communication efficiencies.

关键词： Servers Inspection Data integrity Quality of service distributed databases Costs Time complexity Mobile edge computing edge data integrity smart contract corruption localization inspection algorithm

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Fully Robust Federated Submodel Learning in a distributed Storage System

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IEEE TRANSACTIONS ON INFORMATION THEORY 2025年第4期71卷 3048-3069页

作者： Wang, Zhusheng Ulukus, Sennur Univ Maryland Coll Pk Dept Elect & Comp Engn College Pk MD 20742 USA

We consider the federated submodel learning (FSL) problem in a distributed storage system. In the FSL framework, the full learning model at the server side is divided into multiple submodels such that each selected client needs to download only the required submodel(s) and upload the corresponding update(s) in accordance with its local training data. The server comprises multiple independent databases and the full model is stored across these databases. A fundamental problem in FSL is to enable these multiple databases at the parameter server to collectively maintain a consistent view of the global parameters to facilitate parallel computing across distributed clients. In addition, a practically implementable FSL scheme should possess high throughput, efficient performance, fault tolerance, sufficient privacy, adequate security, certifiable stability, elastic scalability and easy usability features. To specifically resolve the fault tolerance and adequate security issues together, we propose a novel coding mechanism coined ramp secure regenerating coding (RSRC), which is a synthesis of ramp secret sharing and secure regenerating code. This coding technique matches FSL perfectly, as the system performance can be further improved when the full model is stored using RSRC in a distributed manner. By incorporating and extending available techniques cleverly, our new RSRC-based distributed FSL approach that is constructed on top of our earlier two-database FSL scheme which uses private set union (PSU), achieves all of the aforementioned important features. A complete one-round FSL process consists of: 1) an FSL-PSU phase where the union of the submodel indices to be updated by the selected clients in the current round is determined, 2) an FSL-write phase where the updated submodels are written back to the databases, and 3) additional auxiliary phases where sufficient amounts of necessary common randomness are generated at both server and client sides.

关键词： databases Servers Security Encoding Cryptography Throughput Training Data models Training data distributed databases Federated submodel learning parameter server ramp secure regenerating coding private set union

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distributed Estimation of Support Vector Machines for Matrix Data

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第5期35卷 6643-6653页

作者： Xu, Wangli Liu, Jiamin Lian, Heng Renmin Univ China Ctr Appl Stat Sch Stat Beijing 100872 Peoples R China Univ Sci & Technol Beijing Sch Math & Phys Beijing 100083 Peoples R China City Univ Hong Kong Dept Math Hong Kong Peoples R China

Discrimination problems are of significant interest in the machine learning literature. There has been growing interest in extending traditional vector-based machine learning techniques to their matrix forms. In this article, we investigate the statistical properties of the nuclear-norm-based regularized linear support vector machines (SVMs), in particular establishing the convergence rate of the estimator in the high-dimensional setting. Furthermore, within the distributed estimation paradigm, we propose a communication-efficient estimator that can achieve the same convergence rate. We illustrate the performances of the estimators via some simulation examples and an empirical data analysis.

关键词： Support vector machines Convergence Estimation Standards distributed databases Urban areas Learning systems distributed estimator divide and conquer empirical processes high-dimensional matrix regression hinge loss

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Robust Federated Learning for Heterogeneous Clients and Unreliable Communications

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第10期23卷 13440-13455页

作者： Wang, Ruyan Yang, Lan Tang, Tong Yang, Boran Wu, Dapeng Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Adv Network & Intelligent Interconnect Technol Ke Chongqing Educ Commiss China Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Key Lab Ubiquitous Sensing & Networking Chongqing Chongqing 400065 Peoples R China Chongqing Univ Technol Sch Artificial Intelligence Chongqing 400054 Peoples R China

Federated Learning (FL) serves as a machine learning paradigm where distributed devices collaboratively train on local data, with their models subsequently aggregated on a central server. However, challenges arise due to unreliable communication channels, potential sign errors in model parameters, data heterogeneity, and resource limitations that can hinder full client participation. In this paper, firstly, we address these issues by proposing an optimization objective that minimizes FL loss while taking into account constraints on delay and energy consumption. Secondly, to counteract the model drift caused by data heterogeneity and packet errors, we introduce a proximal term in the local training process and incorporate packet errors into the global aggregation phase. Finally, we establish a theoretical convergence upper bound for our FL algorithm in complex non-convex situations, providing insights to guide the formulation of client sampling strategies and ensure FL algorithm convergence. We validate our algorithm's superior accuracy on the MNIST and CIFAR-10 datasets.

关键词： Data models Training Convergence Servers Wireless networks Performance evaluation distributed databases Federated learning heterogeneous clients unreliable channels

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Enabling Industrial Internet of Things by Leveraging distributed Edge-to-Cloud Computing: Challenges and Opportunities

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IEEE ACCESS 2024年 12卷 127294-127308页

作者： Jamil, Mohammad Newaj Schelen, Olov Monrat, Ahmed Afif Andersson, Karl Lulea Univ Technol Dept Comp Sci Elect & Space Engn S-93187 Lulea Sweden

The Industrial Internet of Things (IIoT) promises automation, efficiency, and data-driven decision-making by real-time data collection and analysis. However, traditional IIoT architectures are cloud-centric and, therefore, struggle to handle large volumes of data, edge bandwidth constraints, and data confidentiality. distributed edge-to-cloud computing emerges as a potential solution, also paving the ground for edge-to-cloud data analytics and distributed Artificial Intelligence (AI) to obtain insights for decision-making and predictive maintenance. Despite the potential, however, there is a lack of comprehensive studies identifying key requirements for distributed edge-to-cloud IIoT and analyzing to what extent emerging IoT platforms meet those requirements. The scope of this article is to survey existing literature to identify key requirements in IIoT from the perspective of distributed edge-to-cloud computing. We provide a comparative analysis of three prominent IoT platforms, namely ThingsBoard, Eclipse Ditto, and Microsoft Azure IoT, and assess how these platforms meet the key IIoT requirements. Finally, we identify open challenges and potential research opportunities based on the insights gained from the analysis of the three IoT platforms, thereby setting the stage for future work.

关键词： Industrial Internet of Things Cloud computing Scalability Data analysis Real-time systems distributed databases Big Data Industrial Internet of Things (IIoT) edge-to-cloud computing data analytics IoT platforms

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An Improved Binary Quadratic Discriminant Analysis Classifier by Using Robust Regularization

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IEEE ACCESS 2024年 12卷 114951-114960页

作者： Zaib, Alam Khattak, Shahid Mujtaba, Ghulam Khan, Shahid Al-Rasheed, Amal COMSATS Univ Islamabad Dept Elect & Comp Engn Abbottabad Campus Abbottabad 22060 Pakistan Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst POB 84428 Riyadh 11671 Saudi Arabia

In many real classification problems where a limited number of training samples is available, the linear classifiers based on discriminant analysis are unable to deliver accurate results. Moreover, the testing and/or the training data can be erroneous due to noise contamination which further degrades their performance. Regularization techniques become imperative to deal with these problems. However, the existing regularization techniques, to some extent, mainly focus on data scarcity issues but completely ignore the noisy nature of the testing and/or the training data. We propose a novel regularized quadratic discriminant analysis (R-QDA) classifier which addresses both issues simultaneously. The procedure involves a reformulation of the discriminant function of the conventional QDA classifier into least square problems and then solving them by using regularized least squares (Reg-LS) based on $\ell _{2}$ -norm. In contrast to existing R-QDA techniques, the proposed R-QDA classifier employs two regularization parameters pertaining to each class, which can be independently selected by various robust techniques. Numerical results demonstrate the effectiveness of the proposed method over classical R-QDA methods, especially in high noise regimes.

关键词： Covariance matrices Training data Noise measurement Perturbation methods distributed databases Classification algorithms Discriminant analysis LDA QDA regularization data classification

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Federated Analytics for 6G Networks: Applications, Challenges, and Opportunities

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IEEE NETWORK 2024年第2期38卷 9-17页

作者： Parra-Ullauri, Juan Marcelo Zhang, Xunzheng Bravalheri, Anderson Moazzeni, Shadi Wu, Yulei Nejabati, Reza Simeonidou, Dimitra Univ Bristol Fac Engn Sch Comp Sci Elect & Elect Engn & Engn Maths SCEEM Smart Internet LabHigh Performance Networks Grp Bristol BS8 1QU England

Extensive research is underway to meet the hyperconnectivity demands of 6G networks, driven by applications like XR/VR and holographic communications, which generate substantial data requiring network-based processing, transmission, and analysis. However, adhering to diverse data privacy and security policies in the anticipated multi-domain, multitenancy scenarios of 6G presents a significant challenge. Federated Analytics (FA) emerges as a promising distributed computing paradigm, enabling collaborative data value generation while preserving privacy and reducing communication overhead. Using big data principles, FA can be applied to manage and secure distributed heterogeneous networks, improving performance, reliability, visibility, and security without compromising data confidentiality. This paper provides a comprehensive overview of potential FA applications, domains, and types in 6G networks, elucidating analysis methods, techniques, and queries. It explores complementary approaches to enhance privacy and security in 6G networks alongside FA and discusses the challenges and prerequisites for successful FA implementation. Additionally, distinctions between FA and Federated Learning are drawn, highlighting their synergistic potential through a network orchestration scenario.

关键词： 6G mobile communication Data privacy Security distributed databases Servers Privacy Data analysis Federated learning Federated Analytics 6G Networking Federated Learning

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Accelerating Network Resource Allocation in LoRaWAN via distributed Big Data Computing

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IEEE ACCESS 2024年 12卷 141237-141250页

作者： Spadaccino, Pietro Garlisi, Domenico Franceschi, Andrea Tinnirello, Ilenia Cuomo, Francesca Sapienza Univ Rome Dept Informat Engn Elect & Telecommun DIET I-00184 Rome Italy Consorzio Nazl Interuniv Telecomunicazioni CNIT I-43124 Parma Italy Univ Palermo Dept Math & Informat I-90123 Palermo Italy Univ Palermo Dept Engn I-90128 Palermo Italy

LoRaWAN is a Low Power infrastructure for the Internet of Things (IoT) with a centralized architecture where a single node, the network server, handles all data collection and network management decisions. Given the proliferation and widespread adoption of IoT devices, it becomes essential to incorporate Big Data paradigms at the network server to efficiently manage the enormous volumes of data. In this paper, we introduce a distributed and high-performance methodology for resource allocation in dense LoRaWAN networks, addressing the scalability issues that arise when processing large amounts of information from IoT devices, such as radio link quality. Our contributions establish the groundwork for a distributed implementation of the EXPLORA-C allocation strategy, capable of efficiently operating in large-scale networks. We present two approaches for implementing this distributed scheme: the Multi-Thread (MT) scheme and the Fully-distributed (FD) scheme. Furthermore, we demonstrate the feasibility of this distributed implementation on top of the NebulaStream stream-based end-to-end data management platform. To validate the proposed approach, we exploit our co-simulation framework, EXPLoSIM, where the distributed implementation is fed with data from a simulated LoRaWAN network. This validation shows significant savings in execution time, latency, and scalability. Additionally, we generalize the concept by decomposing a centralized data aggregation scheme into a chain of stream-processing operators, which can be dynamically allocated across device, Edge, and Cloud levels. In the best scenario, our approach improves metrics such as execution time and data reduction by over 90% when compared to its centralized operation.

关键词： LoRaWAN Internet of Things Resource management Big Data Network servers distributed databases Optimization Edge computing Streaming media Big data edge computing fog computing IoT LoRa LPWAN stream data

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FedArtML: A Tool to Facilitate the Generation of Non-IID Datasets in a Controlled Way to Support Federated Learning Research

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IEEE ACCESS 2024年 12卷 81004-81016页

作者： Gutierrez, Daniel Mauricio Jimenez Anagnostopoulos, Aris Chatzigiannakis, Ioannis Vitaletti, Andrea Sapienza Univ Rome Dept Comp Control & Management Engn I-00185 Rome Italy

Federated Learning (FL) enables collaborative training of Machine Learning (ML) models across decentralized clients while preserving data privacy. One of the challenges that FL faces is when the clients' data is not independent and identically distributed (non-IID). It is, therefore, crucial to quantify how non-IID data impacts performance. However, due to the limited number of federated data available, it is not easy to carry out real-world simulations. In this work, we propose for the first time 1) the Hist-Dirichlet-based and Min-Size-Dirichlet methods for partitioning data into multiple nodes using the features and quantity distribution and the Dirichlet distribution. We use the 2) Jensen-Shannon and Hellinger distances for quantifying the degree of IID data. Moreover, we implemented 3) state-of-the-art partitioning methods based on the labels' distribution across clients. All our proposals are open-source in a library called FedArtML, publicly available on PyPI. It facilitates research on cross-silo and cross-device FL, allowing a systematic and controlled partition of centralized datasets using the label, features, and quantity skewness. To demonstrate the value of our proposed methods and the robustness of FedArtML, we experimented in the ECG arrhythmia detection field with Physionet 2020 data. Our results demonstrate that our tool generates federated datasets for multi-client model training and accurately measures client distribution heterogeneity. Our approach achieves 48% higher non-IID-ness than existing feature skew methods, providing more granularity. Furthermore, we validate our simulated federated datasets against real-world data, revealing only a 2% F1-Score difference, affirming the method's real-life applicability.

关键词： Data models Measurement Training Data privacy Systematics Federated learning distributed databases Machine learning Centralized datasets client's heterogeneity federated datasets federated learning heterogeneity metrics machine learning non-IID-ness

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