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Mikhalevskyi, V., and G. Mikhalevska. "TESTING AND FORMATION OF A REPORT ON THE IMPLEMENTATION OF HYBRID INFRASTRUCTURE." Herald of Khmelnytskyi National University. Technical sciences 289, no. 5 (2020): 95–100. https://doi.org/10.31891/2307-5732-2020-289-5-95-100.
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The article considers and investigates the projected results of the hybrid infrastructure formation. Before starting the data transfer, you need to make sure that the infrastructure works properly after the previous settings. To do this, you must perform the series of tests that will detect any failures in different areas. Priority tests are performed globally to verify that the federated trust between the on-premises Exchange Server organization and the Exchange Online cloud service is properly established. As a result of the research, it was found that some objects in the cloud part, when they need to be accessed, have a different system of identifiers than similar objects in the local system. In the local infrastructure they are referred to by the name, while in the cloud infrastructure by the ID. Therefore, in this case, you need to make an additional query that will find the ID-object by its name. When writing a number of codes for data migration, it is proposed to use different approaches to the management of objects in the ground and cloud parts, which prevents the use of the same methods for systems of the same type. Microsoft Exchange exports the data collected during the infrastructure analysis, as well as the data of system users, to files on external media in the form of CSV files to be used by other modules and in the form of TXT or HTML for a spreadsheet that is analyzed by staff. During the transferring data to cloud storage, most companies pay special attention to how security policies will be applied to cloud resources. The main task of this testing stage is to establish that all security parameters exported from the local infrastructure has been imported into the cloud infrastructure successfully and applied at different levels stably. The most important series include tests: related to testing traffic rules and information leakage protection policies; designed to test security policies; tests of policies that affect the client connection. This type of system allows many large companies to avoid the problems associated with the process of migrating global local infrastructure settings to the cloud environment and to improve the data analysis process with subsequent automatic management of data migration in an IT environment with complex network infrastructure.
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Karasawa, Mizuki, John Hover, and Shigeki Misawa. "Federated User Account Management." EPJ Web of Conferences 245 (2020): 07058. http://dx.doi.org/10.1051/epjconf/202024507058.
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BNL SDCC (Scientific Data and Computing Center) recently deployed a centralized identity management solution to support Single Sign On (SSO) authentication across multiple IT systems. The system supports federated login access via CILogon and InCommon and multi-factor authentication (MFA) to meet security standards for various application and services such as Jupyterhub / Invenio that are provided to the SDCC user community. CoManage (cloud-based) and FreeIPA / Keycloak (local) are utilized to provided complex authorization for authenticated users. This talk will focus on technical overviews and strategies to tackle the challenges/obstacles in our facility.
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Goodrich, Michael T., Roberto Tamassia, and Danfeng (Daphne) Yao. "Notarized federated ID management and authentication*." Journal of Computer Security 16, no. 4 (2008): 399–418. http://dx.doi.org/10.3233/jcs-2008-0324.
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Kim, Keunok, Jihyeon Ryu, Hakjun Lee, Youngsook Lee, and Dongho Won. "Distributed and Federated Authentication Schemes Based on Updatable Smart Contracts." Electronics 12, no. 5 (2023): 1217. http://dx.doi.org/10.3390/electronics12051217.
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Federated authentication, such as Google ID, enables users to conveniently access multiple websites using a single login credential. Despite this convenience, securing federated authentication services requires addressing a single point of failure, which can result from using a centralized authentication server. In addition, because the same login credentials are used, anonymity and protection against user impersonation attacks must be ensured. Recently, researchers introduced distributed authentication schemes based on blockchains and smart contracts (SCs) for systems that require high availability and reliability. Data on a blockchain are immutable, and deployed SCs cannot be changed or tampered with. Nonetheless, updates may be necessary to fix programming bugs or modify business logic. Recently, methods for updating SCs to address these issues have been investigated. Therefore, this study proposes a distributed and federated authentication scheme that uses SCs to overcome a single point of failure. Additionally, an updatable SC is designed to fix programming bugs, add to the function of an SC, or modify business logic. ProVerif, which is a widely known cryptographic protocol verification tool, confirms that the proposed scheme can provide protection against various security threats, such as single point of failure, user impersonation attacks, and user anonymity, which is vital in federated authentication services. In addition, the proposed scheme exhibits a performance improvement of 71% compared with other related schemes.
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Venkatraman, Sitalakshmi, and Sazia Parvin. "Developing an IoT Identity Management System Using Blockchain." Systems 10, no. 2 (2022): 39. http://dx.doi.org/10.3390/systems10020039.
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Identity (ID) management systems have evolved based on traditional data modelling and authentication protocols that are facing security, privacy, and trust challenges with the growth of Internet of Things (IoT). Research surveys reveal that blockchain technology offers special features of self-sovereign identity and cryptography that can be leveraged to address the issues of security breach and privacy leaks prevalent in existing ID management systems. Although research studies are recently exploring the suitability of blockchain based support to existing infrastructure, there is a lack of focus on IoT ecosystem in the secured ID management with data provenance of digital assets in businesses. In this paper, we propose a blockchain based ID management system for computing assets in an IoT ecosystem comprising of devices, software, users, and data operations. We design and develop a proof-of-concept prototype using a federated and distributed blockchain platform with smart contracts to support highly trusted data storage and secure authentication of IoT resources and operations within a business case scenario.
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Bhandari, Gayatri M. "FEDNET - A Federated Learning Platform." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem45583.
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Abstract Federated Learning (FL) is a decentralized approach to training machine learning models along with pre- serving data privacy. This paper introduces FedNet, a secure and efficient FL platform designed for model training across distributed clients. FedNet enhances the traditional FL framework by integrating Fernet encryption for secure communication, HTTP and Socket.IO-based real-time client-server interaction, and a JavaFX-powered user interface for ease of deployment. The platform supports multiple FL aggregation techniques, including FedAvg, FedProx, FedNova, and Zona, providing optimized global model updates. FedNet allows machine learning clients to upload, encrypt, and distribute their models securely while allowing clients to train them on local data without the risk of exposing sensitive information. The system provides authentication through model and client ID verification, encrypts model weights before transmission, and aggregates securely the received weights to improve performance. Experimental analysis demonstrates FedNet’s efficiency, scalability, and security in FL-based model training. The proposed platform bridges the gap between privacy-preserving machine learning and real-world deployment, making it suitable for applications in healthcare, finance, and other data-sensitive industries. Keywords: federated learning, machine learning, data privacy, artificial intelligence, data security
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Yusubov, Farkhod, and KangYoon Lee. "A Platform of Federated Learning Management for Enhanced Mobile Collaboration." Electronics 13, no. 20 (2024): 4104. http://dx.doi.org/10.3390/electronics13204104.
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Federated learning (FL) has emerged as a crucial technology in today’s data-centric environment, enabling decentralized machine learning while safeguarding user privacy. This study introduces “Federated Learning ML Operations (FedOps) Mobile”, a novel FL framework optimized for the dynamic and heterogeneous ecosystem of mobile devices. FedOps Mobile addresses the inherent challenges of FL—such as system scalability, device heterogeneity, and operational efficiency—through advanced on-device training using TensorFlow Lite and CoreML. The framework’s innovative approach includes sophisticated client selection mechanisms that assess device readiness and capabilities, ensuring equitable and efficient participation across the network. Additionally, FedOps Mobile leverages remote device control for seamless task management and continuous learning, all without compromising the user experience. The main contribution of this study is the demonstration that federated learning across heterogeneous devices, especially those using different operating systems, can be both practical and efficient using the FedOps Mobile framework. This was validated through experiments that evaluated three key areas: operational efficiency, model personalization, and resource optimization in multi-device settings. The results showed that the proposed method excels in client selection, energy consumption, and model optimization, establishing a new benchmark for federated learning in diverse and complex environments.
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Stergiou, Christos L., Konstantinos E. Psannis, and Brij B. Gupta. "InFeMo: Flexible Big Data Management Through a Federated Cloud System." ACM Transactions on Internet Technology 22, no. 2 (2022): 1–22. http://dx.doi.org/10.1145/3426972.
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This paper introduces and describes a novel architecture scenario based on Cloud Computing and counts on the innovative model of Federated Learning. The proposed model is named Integrated Federated Model , with the acronym InFeMo . InFeMo incorporates all the existing Cloud models with a federated learning scenario, as well as other related technologies that may have integrated use with each other, offering a novel integrated scenario. In addition to this, the proposed model is motivated to deliver a more energy efficient system architecture and environment for the users, which aims to the scope of data management. Also, by applying the InFeMo the user would have less waiting time in every procedure queue. The proposed system was built on the resources made available by Cloud Service Providers (CSPs) and by using the PaaS (Platform as a Service) model, in order to be able to handle user requests better and faster. This research tries to fill a scientific gap in the field of federated Cloud systems. Thus, taking advantage of the existing scenarios of FedAvg and CO-OP, we were keen to end up with a new federated scenario that merges these two algorithms, and aiming for a more efficient model that is able to select, depending on the occasion, if it “trains” the model locally in client or globally in server.
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Dr. C. Prema, Dr. G. Jemilda, Mrs.K. Emily Esther Rani, Santhanamari Abi M, Isabel S, and , Jemima Hannah P. "Loan Shield AI Intelligent ID Verification for Efficient Loan Waivers." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 05 (2025): 2682–87. https://doi.org/10.47392/irjaeh.2025.0398.
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Identity authentication is a critical component in the processing of government loan waivers. Manual authentication tends to cause errors and forgery, thus delaying the approval of the loans. To address this, a smart system named Loan Shield AI has been created. It utilizes deep learning, OCR, and federated learning to authenticate documents such as Aadhaar and Smart Cards and auto-extract crucial details. A voice assistant is also provided to assist users by assisting them and completing loan forms from the extracted data. The system is containerized in an application for seamless deployment and tested for document verification, face matching, and fraud detection. Automation and voice support make the loan application quick, precise, and user-friendly. The system also accommodates various document types and various languages to enhance usability and dependability.
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L'Amrani, Hasnae, Younès El Bouzekri El Idrissi, and Rachida Ajhoun. "Technical Interoperability to Solve Cross-Domain Issues Among Federation Systems." International Journal of Smart Security Technologies 7, no. 1 (2020): 21–40. http://dx.doi.org/10.4018/ijsst.2020010102.
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Digital identity management with the metamorphosis of web services enforces new security challenges. A set of identity management systems exists to deal with these identities, alongside the goal of improving user experience and gain secure access. Nowadays, one faces a large number of heterogeneous identity management approaches. This study treated several identity management systems. The federated system makes proof of it eligibility for the identity management. Thus, the researcher interest is on the federated model. Since it consists of the distribution of digital identity between different security domains. The base of security domains is a trust agreement between the entities in communication. Federated identity management faces the problem of interoperability between heterogeneous federated systems. This study is an approach of a technical interoperability between the federations. The authors propose an approach that will permit inter-operation and exchange identity information among heterogeneous federations.
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Burka, Ganga Reddy, Anusha Chakali, Meera Alphy Dr., and Shirisha K. "Employee Management GUI." Advanced Innovations in Computer Programming Languages 6, no. 3 (2024): 30–37. https://doi.org/10.5281/zenodo.12772466.
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<em>The provided code implements an Employee Management System using the Tkinter library for building the graphical user interface (GUI) and SQLite3 for database management. The system allows users to perform various operations such as adding new employees, removing existing employees, searching for employees, and adding remarks to employee records. The application's main window features a Treeview widget displaying employee information including ID, name, and remarks. It provides buttons for adding, removing, searching, and adding remarks to employee records. User input is facilitated through dialog boxes for entering employee details and prompts for employee ID. The code utilizes object-oriented programming principles with an Employee Management App class encapsulating the application's functionality. Methods within the class handle database connection, table creation, employee manipulation, and GUI updates. Additionally, the application ensures data integrity by checking for duplicate employee IDs during addition and provides informative messages for user interactions. Overall, the Employee Management System provides a simple yet effective interface for managing employee records with basic CRUD (Create, Read, Update, Delete) functionalities.</em>
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Prof. Sonali Patil, Parth Tagadpallewar, Nayan Pagare, Neha Raut, and Varad Tagadpallewar. "Alpha Identification Based Otp System." International Research Journal on Advanced Engineering Hub (IRJAEH) 2, no. 03 (2024): 677–82. http://dx.doi.org/10.47392/irjaeh.2024.0098.
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In the realm of contemporary digital security, this study focuses on enhancing Secure OTP Management. The Alphabetical ID system is a pivotal solution to bolster security and streamline user authentication. It strategically displays three alphabetic characters in both the generated OTP sent to the user and the interface for OTP input. This design empowers users to identify the latest OTP among received codes, addressing the dilemma of consecutive OTPs causing confusion and potential access denial. The Alphabetical ID system revolutionizes identification protocols, significantly reducing the likelihood of unauthorized access. This heightened identification capability serves as a robust safeguard for sensitive data and reinforces the integrity of digital transactions. The synergy between the Alphabetical ID system and the conventional OTP process marks a paradigm shifts in user authentication, achieving an optimal balance between user experience and identification precision. As businesses transition towards digitization and online interactions, the Alphabetical ID system emerges as a pioneering leap, offering a reliable shield against unauthorized access and catalyzing fortified digital security. This inventive solution stands as a pragmatic answer to the challenge of identification in OTP management, culminating in an elegant fusion of simplicity and efficacy. The proposed Alphabetical ID system is poised to redefine the landscape of Secure OTP Management by placing a heightened emphasis on user-friendly identification mechanisms within the broader context of digital security.
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Shukla, Sheetakshi, and Kirti Jain. "Rise of Identity and Access Management with Microsoft Security." International Journal on Advances in Engineering, Technology and Science (IJAETS) 5, no. 1 (2024): 1–7. https://doi.org/10.5281/zenodo.10621038.
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<strong>Abstract—</strong> Identity and Access Management (IAM) is a pivotal element in modern cybersecurity strategies, enabling organizations to manage user identities and control access to digital resources securely. This paper focuses on Microsoft's comprehensive suite of IAM solutions, emphasizing the innovative capabilities of Microsoft Entra ID as a central component within its ecosystem. The discussion spans Entra ID's role in IAM, Multi-Factor Authentication (MFA), Conditional Access policies, and Microsoft Entra Privileged Identity Management (PIM). This research explores the dynamic landscape of IAM in the context of Microsoft security, addressing challenges and opportunities posed by contemporary cybersecurity threats and evolving work environments. Key topics include the integration of IAM solutions with Microsoft 365 services, the impact of remote work on identity governance, and the effective implementation of conditional access policies to enhance security without compromising user experience.Furthermore, the paper investigates the role of IAM, specifically Microsoft Entra ID, in meeting security and compliance requirements. It delves into data protection, threat intelligence, and compliance reporting within the Entra ID framework. As organizations navigate hybrid environments that span on-premises and cloud infrastructures, the research examines the intricacies of managing user authentication in such diverse setups. The study concludes by emphasizing the importance of adapting IAM strategies continuously to address evolving cybersecurity challenges within Microsoft's security ecosystem. By referencing the latest Microsoft Entra ID documentation and industry best practices, this research contributes to a deeper understanding of the significance of IAM, specifically Microsoft Entra ID, and its practical implications for organizations seeking robust identity and access management solutions. <strong>Keywords—</strong> <em>IAM, Microsoft Entra ID, MFA, Conditional Access, Identity Governance</em>
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Gao, Chao, and Xiaobing Hu. "Design and Implementation of Home Health System Based on ID Card Identification and Multidevice Access." Wireless Communications and Mobile Computing 2021 (April 13, 2021): 1–11. http://dx.doi.org/10.1155/2021/5514687.
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With the development of society and the gradual arrival of an ageing society, various diseases threaten human health. If you use the software that comes with your medical device to understand and analyze your health, not only the user is in trouble but also a variety of health data can not be linked, giving users a more three-dimensional health analysis. In this case, this article will design and implement a mobile client for a home health system. The user’s information is hosted by the remote data center. It is necessary to collect the user’s information to facilitate management and analysis of health conditions. The manual input method is easy to input errors due to the long address and ID number, so the system will collect user information based on ID card identification. Train ID information to achieve accurate identification. For the multidevice access function, the key parts of the IEEE 11073 standard are used for communication. The mobile phone client that implements the home health system runs on the Android platform. According to the user information management, the user physiological data management, the user physiological data collection, and the user information synchronization are divided into four modules. The ID card identification module is called in the user management module, and the IEEE11073 communication plug-in is called by JNI in the health data measurement module. The health data can be presented intuitively to the user, or the information can be synchronized to the remote data center for further analysis.
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Rajarajeswari, C. S., and M. Aramudhan. "User Opinion and Differentiated Attribute based Ranking in Federated Cloud." Journal of Information Technology Research 9, no. 2 (2016): 78–88. http://dx.doi.org/10.4018/jitr.2016040104.
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Cloud computing is an innovative technology which provides services to users on-demand and pay per use. Since there are many providers in cloud, users get confused in selecting the optimal service provider for their tasks. To overcome this limitation, federated cloud management architecture was proposed. The proposed work provides a new federated cloud mechanism, in which Broker Manager takes the responsibility of providing optimal and ranked service provider for user requirements. To rank the service providers in the federated cloud, Differentiated Priority based Ranking algorithm is implemented at the level of BM. Attributes are differentiated based on their weights assigned by a user. Service providers are discovered and ranked based on the differentiated attributes. The proposed algorithm chooses the cloud service provider for execution, not only based on the rank list generated by the BM; but also based on the suggestion given by the user. The experimental result shows that the proposed algorithm improves the performance of resource provisioning than the existing model by 13%.
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Ou, Wei, Jianhuan Zeng, Zijun Guo, Wanqin Yan, Dingwan Liu, and Stelios Fuentes. "A homomorphic-encryption-based vertical federated learning scheme for rick management." Computer Science and Information Systems 17, no. 3 (2020): 819–34. http://dx.doi.org/10.2298/csis190923022o.
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With continuous improvements of computing power, great progresses in algorithms and massive growth of data, artificial intelligence technologies have entered the third rapid development era. However, With the great improvements in artificial intelligence and the arrival of the era of big data, contradictions between data sharing and user data privacy have become increasingly prominent. Federated learning is a technology that can ensure the user privacy and train a better model from different data providers. In this paper, we design a vertical federated learning system for the for Bayesian machine learning with the homomorphic encryption. During the training progress, raw data are leaving locally, and encrypted model information is exchanged. The model trained by this system is comparable (up to 90%) to those models trained by a single union server under the consideration of privacy. This system can be widely used in risk control, medical, financial, education and other fields. It is of great significance to solve data islands problem and protect users? privacy.
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Harris, Nicole. "Would you eduID? Improving the user experience of federated access management." Serials: The Journal for the Serials Community 23, no. 2 (2010): 159–62. http://dx.doi.org/10.1629/23159.
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Yogeswara, Reddy Avuthu. "Federated Identity and Single Sign-On (SSO): Balancing Security and Usability in Cloud IAM Implementations." Journal of Scientific and Engineering Research 6, no. 6 (2019): 239–47. https://doi.org/10.5281/zenodo.14274255.
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Cloud services have become ubiquitous, and the growing complexity of managing user identities across multiple platforms introduces security challenges. Federated Identity and Single Sign-On (SSO) mechanisms aim to simplify the user experience by enabling seamless access across multiple domains while maintaining security. This paper explores the trade-offs between security and usability in federated identity implementations and offers insights into best practices for balancing these concerns. Several use cases and solutions from literature between 2015–2018 are discussed.
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Wu, Guile, and Shaogang Gong. "Decentralised Learning from Independent Multi-Domain Labels for Person Re-Identification." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 4 (2021): 2898–906. http://dx.doi.org/10.1609/aaai.v35i4.16396.
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Deep learning has been successful for many computer vision tasks due to the availability of shared and centralised large-scale training data. However, increasing awareness of privacy concerns poses new challenges to deep learning, especially for human subject related recognition such as person re-identification (Re-ID). In this work, we solve the Re-ID problem by decentralised learning from non-shared private training data distributed at multiple user sites of independent multi-domain label spaces. We propose a novel paradigm called Federated Person Re-Identification (FedReID) to construct a generalisable global model (a central server) by simultaneously learning with multiple privacy-preserved local models (local clients). Specifically, each local client receives global model updates from the server and trains a local model using its local data independent from all the other clients. Then, the central server aggregates transferrable local model updates to construct a generalisable global feature embedding model without accessing local data so to preserve local privacy. This client-server collaborative learning process is iteratively performed under privacy control, enabling FedReID to realise decentralised learning without sharing distributed data nor collecting any centralised data. Extensive experiments on ten Re-ID benchmarks show that FedReID achieves compelling generalisation performance beyond any locally trained models without using shared training data, whilst inherently protects the privacy of each local client. This is uniquely advantageous over contemporary Re-ID methods.
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B S, Rajeshwari, M. Dakshayini, and H. S. Guruprasad. "Efficient Task Scheduling and Fair Load Distribution Among Federated Clouds." Journal of ICT Research and Applications 15, no. 3 (2021): 216–38. http://dx.doi.org/10.5614/itbj.ict.res.appl.2021.15.3.2.
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The federated cloud is the future generation of cloud computing, allowing sharing of computing and storage resources, and servicing of user tasks among cloud providers through a centralized control mechanism. However, a great challenge lies in the efficient management of such federated clouds and fair distribution of the load among heterogeneous cloud providers. In our proposed approach, called QPFS_MASG, at the federated cloud level, the incoming tasks queue are partitioned in order to achieve a fair distribution of the load among all cloud providers of the federated cloud. Then, at the cloud level, task scheduling using the Modified Activity Selection by Greedy (MASG) technique assigns the tasks to different virtual machines (VMs), considering the task deadline as the key factor in achieving good quality of service (QoS). The proposed approach takes care of servicing tasks within their deadline, reducing service level agreement (SLA) violations, improving the response time of user tasks as well as achieving fair distribution of the load among all participating cloud providers. The QPFS_MASG was implemented using CloudSim and the evaluation result revealed a guaranteed degree of fairness in service distribution among the cloud providers with reduced response time and SLA violations compared to existing approaches. Also, the evaluation results showed that the proposed approach serviced the user tasks with minimum number of VMs.
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Rao, M. Koteswara, B. Pranathi, G. Samyuktha, K. Kruthik, and G. Varshitha. "Examination Marks Management for VNRVJIET." International Journal for Research in Applied Science and Engineering Technology 11, no. 10 (2023): 1799–803. http://dx.doi.org/10.22214/ijraset.2023.52104.
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Abstract: With the increase in the number of branches, students, and examinations at VNRVJIET, a more efficient examination marks managementsystem is necessary. Currently, manually keeping track of all the examination data is time-consuming, prone to mistakes, and lacks a user-friendly interface. To address these shortcomings, we developed a new Examination Marks Management System. Our solution is a web application designed to manage the marks of the Semester End Theory and Laboratory examinations at VNRVJIET. We utilized uploaded images to extract student data and marks, adding checkpoints to validate errors at each step of the process. The system also maps examination data to student ID data and generates necessary reports. Our team worked on the entire exam marks management system, from creating an exam and generating an exam ID to extracting marks data and storing it in a database. The web application we developed is user-friendly and provides a more accurate way to manage examination data. In summary, our new Examination MarksManagement System offers an efficient way to manage examination data at VNRVJIET. It eliminates the challenges associated with manual record keeping, reduces errors, and provides an intuitive user interface. With this system in place, VNRVJIET can easily manage examination data for its growing number of branches, students, and examinations.
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Preetham Kumar Dammalapati. "Understanding federated identity management: Architecture, protocols and implementation." World Journal of Advanced Engineering Technology and Sciences 15, no. 3 (2025): 401–11. https://doi.org/10.30574/wjaets.2025.15.3.0919.
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Federated Identity Management (FIM) emerges as a critical solution for organizations navigating the complexities of modern digital environments, where identity management across disparate systems presents significant security challenges. By establishing trust relationships between identity providers and service providers, FIM enables seamless authentication across organizational boundaries while maintaining robust security controls. This comprehensive framework eliminates redundant authentication processes, reduces credential proliferation, and addresses the fragmentation issues inherent in multi-cloud environments. The architecture encompasses identity providers, service providers, trust frameworks, and claims mechanisms working in concert through standardized protocols such as OAuth 2.0, OpenID Connect, SAML, and WS-Federation. FIM delivers transformative benefits including enhanced user experience through Single Sign-On capabilities, strengthened security posture via centralized authentication, and substantial operational efficiencies. While implementation considerations such as just-in-time provisioning, attribute mapping, session management, and trust chain security present notable challenges, various architectural patterns including hub-and-spoke, mesh federation, and broker models offer flexible deployment options to match organizational requirements. As digital transformation accelerates, emerging trends such as decentralized identity, continuous authentication, and Zero Trust integration are reshaping the federation landscape.
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Bordoloi, Dibyahash. "Import and Export Database Management System." Mathematical Statistician and Engineering Applications 70, no. 1 (2021): 182–89. http://dx.doi.org/10.17762/msea.v70i1.2298.
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Project components include login, customer registration, add products, view products, purchase order, sales order, payment, and report in the import export management system. Import manager, customers, and suppliers must provide their user name and password to log in to the login module. Supplier will enter information about the product, including its name, category, price, selling price, and quantity, in the add products module. The user may examine product information in the view products module. Using the database's product id, customers can create purchase orders in the purchase order module. The supplier will validate the product's purchase order in the sales order module. When a client makes a payment using the payment module, the money is deducted from their account once the payment has been confirmed based on the customer's name, bank name, account number, company name, and customer ID. Import manager will provide reports for customer information, product information, purchase order, sales order, and supplier information in the report module.
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Satchell, C., G. Shanks, S. Howard, and J. Murphy. "Identity crisis: user perspectives on multiplicity and control in federated identity management." Behaviour & Information Technology 30, no. 1 (2011): 51–62. http://dx.doi.org/10.1080/01449290801987292.
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Xiao, Yong, Xin Jin, Tingzhe Pan, Zhenwei Yu, and Li Ding. "A Federated Learning Algorithm That Combines DCScaffold and Differential Privacy for Load Prediction." Energies 18, no. 6 (2025): 1482. https://doi.org/10.3390/en18061482.
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Accurate residential load forecasting plays a crucial role in optimizing demand-side resource integration and fulfilling the needs of demand-side response initiatives. To tackle challenges, such as data heterogeneity, constrained communication resources, and data security in smart grid load prediction, this study introduces a novel differential privacy federated learning algorithm. Leveraging the federated learning framework, the approach incorporates weather and temporal factors as key variables influencing load patterns, thereby creating a privacy-preserving load forecasting solution. The model is built upon the Long Short-Term Memory (LSTM) network architecture. Experimental results demonstrate that the proposed algorithm enables federated training without the need for sharing raw load data, facilitating load scheduling and energy management operations in smart grids while safeguarding user privacy. Furthermore, it exhibits superior prediction accuracy and communication efficiency compared to existing federated learning methods.
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Turgay, Safiye. "Blockchain Management and Federated Learning Adaptation on Healthcare Management System." International Journal of Intelligent Systems and Applications 14, no. 5 (2022): 1–13. http://dx.doi.org/10.5815/ijisa.2022.05.01.
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Recently, health management systems have some troubles such as insufficient sharing of medical data, security problems of shared information, tampering and leaking of private data with data modeling probes and developing technology. Local learning is performed together with federated learning and differential entropy method to prevent the leakage of medical confidential information, so blockchain-based learning is preferred to completely eliminate the possibility of leakage while in global learning. Qualitative and quantitative analysis of information can be made with information entropy technology for the effective and maximum use of medical data in the local learning process. The blockchain is used the distributed network structure and inherent security features, at the same time information is treated as a whole, not as islands of data. All the way through this work, data sharing between medical systems can be encouraged, access records tampered with, and better support medical research and definitive medical treatment. The M/M/1 queue for the memory pool and M/M/C queue to combine integrated blockchains with a unified learning structure. With the proposed model, the number of transactions per block, mining of each block, learning time, index operations per second, number of memory pools, waiting time in the memory pool, number of unconfirmed transactions in the whole system, total number of transactions were examined. Thanks to this study, the protection of the medical privacy information of the user during the service process and the autonomous management of the patient’s own medical data will benefit the protection of privacy within the scope of medical data sharing. Motivated by this, proposed a blockchain and federated learning-based data management system able to develop in next studies.
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Zhu, Hejun, and Liehuang Zhu. "Traffic Identification Based on User Access Authentication and Message Tag." Journal of Computational and Theoretical Nanoscience 14, no. 1 (2017): 1–6. http://dx.doi.org/10.1166/jctn.2017.6114.
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In order to solve the problem of direct location management of Internet users under the environment of NAT, the unique session ID that represents the user session was put the first option location of the initial session SYN option, and the same session ID was put the additional tail part of the each initial UDP session and the specified UDP tail part, the traffic identification and location management of the Internet users were realized under the environment of NAT through the message tag and user access authentication, the practical application showed that compared with the traditional method, this method not only solved the location management of the Internet users under the NAT, but also greatly reduced the Internet authentication device, network mirror device, network transmission device, network bandwidth and other resources.
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Rind, Ofer, Douglas Benjamin, Lincoln Bryant, et al. "The Creation and Evolution of the US ATLAS Shared Analysis Facilities." EPJ Web of Conferences 295 (2024): 07043. http://dx.doi.org/10.1051/epjconf/202429507043.
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Prior to the start of the LHC Run 3, the US ATLAS Software and Computing operations program established three shared Tier 3 Analysis Facilities (AFs). The newest AF was established at the University of Chicago in the past year, joining the existing AFs at Brookhaven National Lab and SLAC National Accelerator Lab. In this paper, we will describe both the common and unique aspects of these three AFs, and the resulting distributed facility from the user’s perspective, including how we monitor and measure the AFs. The common elements include enabling easy access via Federated ID, file sharing via EOS, provisioning of similar Jupyter environments using common Jupyter kernels and containerization, and efforts to centralize documentation and user support channels. The unique components we will cover are driven in turn by the requirements, expertise and resources at each individual site. Finally, we will highlight how the US AFs are collaborating with other ATLAS and LHC wide (IRIS-HEP and HSF) user analysis support activities, evaluating tools like ServiceX and Coffea-Casa.
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Cao, Yuan, Lin Yang, Zom Bo Fu, and Feng Yang. "Identity Management Architecture: Paradigms and Models." Applied Mechanics and Materials 40-41 (November 2010): 647–51. http://dx.doi.org/10.4028/www.scientific.net/amm.40-41.647.
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This paper provides an overview of identity management architecture from the viewpoint of paradigms and models. The definition of identity management architecture has been discussed, paradigms are classified by the development stage and core design principle transmission of the architecture which include network centric paradigm, service centric paradigm, and user centric paradigm; models are grouped by components varying and functions changing to isolated model, centralized model, and federated model. These paradigms and models have no collisions among them for they are views of identity management from different viewpoint.
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Xu, Bowen, Zhijintong Zhang, Aozhuo Sun, et al. "T-FIM: Transparency in Federated Identity Management for Decentralized Trust and Forensics Investigation." Electronics 12, no. 17 (2023): 3591. http://dx.doi.org/10.3390/electronics12173591.
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Federated Identity Management (FIM) has gained significant adoption as a means to simplify user authentication and service authorization across diverse domains. It serves as a centralized authentication and authorization method, enabling users to access various applications or resources using credentials issued by a universally trusted identity provider (IdP). However, recent security incidents indicate that the reliability of credentials issued by IdP is not absolute in practice. If the IdP fails, it can persistently access any application that trusts it as any user. This poses a significant security threat to the entire system. Furthermore, with the increasing adoption of FIM across diverse scenarios, there is a growing demand for the development of an identity management system that can effectively support digital forensics investigations into malicious user behavior. In this work, we introduce transparency to federated identity management, proposing T-FIM to supervise unconditional trust. T-FIM employs privacy-preserving logs to record all IdP-issued tokens, ensuring that only the true owner can access the exact token. We utilize identity-based encryption (IBE), but not just as a black box, encrypting tokens before they are publicly recorded. In addition, we propose a decentralized private key generator (DPKG) to provide IBE private keys for users, avoiding the introduction of a new centralized trust node. T-FIM also presents a novel approach to digital forensics that enables forensic investigators to collect evidence in a privacy-preserving manner with the cooperation of the DPKG. We conduct a comprehensive analysis of the correctness, security, and privacy aspects of T-FIM. To demonstrate the practical feasibility of T-FIM, we evaluated the additional overhead through experimental evaluations. Additionally, we compared its performance with other similar schemes to provide a comprehensive understanding of its capabilities and advantages.
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Hua, Shaona, Chunying Zhang, Guanghui Yang, et al. "An FTwNB Shield: A Credit Risk Assessment Model for Data Uncertainty and Privacy Protection." Mathematics 12, no. 11 (2024): 1695. http://dx.doi.org/10.3390/math12111695.
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Credit risk assessment is an important process in bank financial risk management. Traditional machine-learning methods cannot solve the problem of data islands and the high error rate of two-way decisions, which is not conducive to banks’ accurate credit risk assessment of users. To this end, this paper establishes a federated three-way decision incremental naive Bayes bank user credit risk assessment model (FTwNB) that supports asymmetric encryption, uses federated learning to break down data barriers between banks, and uses asymmetric encryption to protect data security for federated processes. At the same time, the model combines the three-way decision methods to realize the three-way classification of user credit (good, bad and delayed judgment), so as to avoid the loss of bank interests caused by the forced division of uncertain users. In addition, the model also incorporates incremental learning steps to eliminate training samples with poor data quality to further improve the model performance. This paper takes German Credit data and Default of Credit Card Clients data as examples to conduct simulation experiments. The result shows that the performance of the FTwNB model has been greatly improved, which verifies that it has good credit risk assessment capabilities.
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Vikas K Kolekar. "Adaptive Middleware to Analyze and Confirm Data Centric Parameters for Data Aggregation in Federated Cloud Environment." Panamerican Mathematical Journal 34, no. 3 (2024): 01–14. http://dx.doi.org/10.52783/pmj.v34.i3.1709.
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In the last decade rapid data evolution has contributed to tremendous data movement. The efficient data movement from origin to cloud storage and vice versa leads to the significant performance of application. A software layer situated between user and cloud storage called middleware plays major role in handling data to full fill user goals and requirements. In this paper, proposed a development of middleware frameworks from generic to context aware framework. Explicit importance is given to the adaptation feature of the middleware. A preliminary background is presented to list out importance and details of adaptability of the middleware and curious differentiation in static and self-adaptive adaptation features of the middleware. The literature started with the description of many generic middleware which can be used for building applications in variety of domains and further the limitations imposed are exposed. The issues like multidisciplinary data, vendor lock-in, disparity of service and interoperability related to federated cloud services serving to the fast-developing technology are highlighted. Along with that the use of context aware data management techniques for efficient handling of federated cloud system are defined. An extensive and comprehensive analysis of context-aware middleware is presented. For skilful data management processes need of context aware application with self-adaptation capability is presented especially in response to dynamic situations in federated cloud environment. At the end three techniques to design and develop context-aware middleware are presented. Alongside, four different perspectives of realizing adaptive middleware are also detailed. The presented work concludes with the highlighted need of context aware middleware framework to enhance the performance of federated cloud system.
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Popowich, Sam. "Proxying the Data Body: Artificial Intelligence, Federated Identity, and Machinic Subjection." Journal of Contemporary Issues in Education 15, no. 1 (2020): 35–50. http://dx.doi.org/10.20355/jcie29410.
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Academic libraries have recently seen a shift from self-management of user-authentication of licensed resources themselves, to cloud-based implementations of "federated identity" technologies. Such technologies aim to solve the problems of fragile access to licensed resources while also better protecting publishers' intellectual property. However, federated identity systems raise a host of issues regarding privacy, surveillance, machinic subjection, and algorithmic governance. This paper traces the development of federated identity systems out of earlier authentication processes, shows how such systems use artificial intelligence techniques to create a trackable "data body" for each student, and then analyzes this whole procedure through the critical theories of Maurizio Lazzarato and Bernard Stiegler. In conclusion, the article argues that the emergent nature of the "data body" creates ambiguity between the hyper-control of contemporary technologies and the possibility of resisting them.
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L.Swathi, K., P. Divya, A. Amruthavarshini, and Dr B.VijayaBabu. "Implementation of Identity Management Using Open ID Protocol." International Journal of Engineering & Technology 7, no. 2.32 (2018): 104. http://dx.doi.org/10.14419/ijet.v7i2.32.13538.
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Identity Management has turned into an imperative theme in the distributed computing conditions, where cloud suppliers need to control usernames, passwords and other data used to recognize, validate and approve clients for some, unique facilitated applications. Every one of the vulnerabilities seen on non-cloud arrangements are presently found in the cloud, yet different issues are presented. One would be the capacity to oversee characters of clients when sending information to the cloud .Second would be the identity administration of clients accepting information from the cloud. Furthermore, third would be administration of user id’s when information is moved from cloud to cloud. Open ID is an open standard and decentralized confirmation protocol. Promoted by the non-benefit Open ID Foundation, it enables clients to be verified by co-working locales (known as Relying Parties or RP) utilizing an outsider administration, taking out the requirement for website admin to give their own particular specially appointed login frameworks, and enabling clients to sign into numerous inconsequential sites without having a different personality and secret key for each.
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Mrs., Deepthi S., and Dr.R.Chinnayan. "A Survey on Federated Learning for Intelligent Healthcare Systems." International Journal of Engineering Research and Reviews 11, no. 4 (2023): 8–17. https://doi.org/10.5281/zenodo.10040680.
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<strong>Abstract:</strong><i> </i>The accelerated growth of Artificial Intelligence (AI) has greatly influenced the healthcare industry, offering significant advancements in smart healthcare systems. However, the lack of standards, legal regulations, and is difficult to meet ethical standards for patient information privacy. The utilization of large quantities of user data for training machine learning models has shown promising results. Nonetheless, two major obstacles persist: the fragmented nature of user data, hindering aggregation without compromising privacy, and the failure of cloud-based models to personalize healthcare. To address these issues, Federated Learning (FL) has emerged as a solution, leveraging privacy-preserving algorithms to overcome data atomization concerns. Furthermore, integrating FL with technologies like blockchain and edge computing can enhance security and computational efficiency.This paper presents an overview of FL architectures, comparing many kinds of federated learning frameworks and distributed machine learning algorithms. It explores the limitations of current smart healthcare systems and highlights how FL can overcome these challenges. The study investigates different FL architectures and classification models, showcasing their potential application in healthcare.Furthermore, it analyses the advantages of FL in medical settings, emphasizing privacy preservation and improved data management. The paper also assesses the security risks associated with healthcare applications and proposes ways to mitigate them. The research findings aim to help both academia and industry understand the competitive advantage offered by advanced privacy-preserving federated learning systems in the field of healthcare data.<strong>Keywords:</strong><i> </i>Artificial Intelligence (AI), Federated Learning, Privacy Preservation, Data Management, Security Risks.<strong>Title:</strong> A Survey on Federated Learning for Intelligent Healthcare Systems<strong>Author:</strong> Mrs. Deepthi S, Dr.R.Chinnayan<strong>International Journal of Engineering Research and Reviews</strong><strong>ISSN 2348-697X (Online)</strong><strong>Vol. 11, Issue 4, October 2023 - December 2023</strong><strong>Page No: 8-17</strong><strong>Research Publish Journals</strong><strong>Website: www.researchpublish.com</strong><strong>Published Date: 25-October-2023</strong><strong>DOI: </strong><strong>https://doi.org/10.5281/zenodo.10040680</strong><strong>Paper Download Link (Source)</strong><strong>https://www.researchpublish.com/papers/a-survey-on-federated-learning-for-intelligent-healthcare-systems</strong>
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Leila Mohamed Abdelgayoum Abdelgader. "FedDeepRiskNet: A Federated Learning Framework for Secure and Efficient Multi-Hospital Management." Journal of Information Systems Engineering and Management 10, no. 20s (2025): 277–87. https://doi.org/10.52783/jisem.v10i20s.3053.
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Existing multi-hospital management frameworks, especially those that combine vital sign IoT data with elasticity approaches, often have data storage, poor predictive skills, and poor human-centered design. This study tackles these issues and provide safe and private data exchange, by proposing a novel framework that works with a federated learning technique. Federated learning is a decentralized machine learning (ML) method that enables several hospitals to work together on model training without compromising the privacy of their patient data. The proposed approach directly addresses the issue of data storage by integrating the work with a deep learning (DL) algorithm. The aim of this research is to improve resources allocation, improve patient outcomes, and find diseases earlier. The DL method employed is a more sophisticated and more effective method of patient risk categorization. This framework greatly contributes to the development of healthcare 4.0 by allowing more effective, equitable, and patient-centered care across multi-hospital networks by solving the issues of data interoperability, improving prediction accuracy, and placing a priority on user experience.
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Nair, Sreejith Sreekandan, and Govindarajan Lakshmikanthan. "Securing Autonomous Vehicles: Blockchain and Federated Identity Solutions for Seamless Authentication." International Journal for Research in Applied Science and Engineering Technology 12, no. 12 (2024): 1858–71. https://doi.org/10.22214/ijraset.2024.66103.
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Abstract: Autonomous Vehicles (AVs) have revolutionized the transportation landscape but have come with serious challenges to cybersecurity. This is to ensure the vehicular network is maintained and prevent any unauthorized access. In this paper, we explore how blockchain technology could be integrated as a robust solution to secure and enhance the efficiency of the AV authentication process using federated identity management within the AV community. Blockchain is an immutable, decentralized ledger of data, and its integrity and transparency are ensured throughout vehicular networks. Federated identity management presents a single console for authentication, whereby different systems authenticate entities without compromising security or privacy. Together, these technologies build a framework that tackles such fundamental issues as data tampering, authentication latency, and lack of peripheral vulnerability (centralized vector). The hybrid methodology of blockchain for data validation and federated identity for efficient authentication of the user and vehicle is presented. Algorithms and mathematical models are derived to illustrate the framework’s functionality. Simulation results show that authentication speed, scalability, and resistance to cyberattacks are improved significantly than the traditional methods. The proposed system satisfies the security needs of AV ecosystems and paves the way for incorporating AI-driven threat detection. Blockchain and federated identity solutions promise to provide the security and reliability needed to support autonomous transportation systems, and this paper underscores this transformative capability
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Alqubaysi, Tariq, Abdullah Faiz Al Asmari, Fayez Alanazi, Ahmed Almutairi, and Ammar Armghan. "Federated Learning-Based Predictive Traffic Management Using a Contained Privacy-Preserving Scheme for Autonomous Vehicles." Sensors 25, no. 4 (2025): 1116. https://doi.org/10.3390/s25041116.
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Intelligent Transport Systems (ITSs) are essential for secure and privacy-preserving communications in Autonomous Vehicles (AVs) and enhance facilities like connectivity and roadside assistance. Earlier research models used for traffic management compromised user privacy and exposed sensitive data to potential adversaries while handling real-time data from numerous vehicles. This research introduces a Federated Learning-based Predictive Traffic Management (FLPTM) system designed to optimize service access and privacy for Autonomous Vehicles (AVs) within an ITS. Moreover, a CPPS will provide strong security to mitigate adversarial threats through state modelling and authenticated access permissions for the integrity of vehicle communication networks from man-in-the-middle attacks. The suggested FLPTM system utilizes a Contained Privacy-Preserving Scheme (CPPS) that decentralizes data processing and allows vehicles to train local models without sharing raw data. The CPPS framework combines a classifier-based learning technique with state modelling and access permissions to protect user data against invasions and man-in-the-middle attacks. The proposed model leverages Federated Learning (FL) to enhance data security in collaborative machine learning processes by allowing updates that preserve privacy, enabling joint learning without exposing raw data. It addresses key challenges such as high communication costs, the impact of adversarial attacks, and access time inefficiencies. Using FL, the model reduces communication costs by 23.29%, mitigates adversarial effects by 16.1%, and improves access time by 18.95%, achieving significant cost savings and maintaining data privacy throughout the learning process.
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Akpe, Oyinomomo-emi Emmanuel, Denis Kisina, Samuel Owoade, Abel Chukwuemeke Uzoka, right Chibunna Ubanadu, and Andrew Ifesinachi Daraojimba. "Advances in Federated Authentication and Identity Management for Scalable Digital Platforms." Journal of Frontiers in Multidisciplinary Research 2, no. 1 (2021): 87–93. https://doi.org/10.54660/.ijfmr.2021.2.1.87-93.
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The increasing complexity of digital platforms, driven by cloud-native architectures, distributed applications, and user-centric services, necessitates robust, scalable, and secure identity management frameworks. This paper explores recent advances in federated authentication and identity management, emphasizing their role in enabling seamless and secure access across interconnected digital ecosystems. Beginning with a foundational overview of identity management’s evolution—from traditional siloed systems to federated and decentralized models—the study outlines key technologies such as SAML, OAuth, OpenID Connect, and emerging paradigms like decentralized identity and blockchain-based verification. It further investigates the integration of artificial intelligence and machine learning for adaptive authentication, anomaly detection, and risk-based decision-making, alongside privacy-enhancing technologies ensuring compliance with data protection regulations such as GDPR. Through the examination of scalability, interoperability, and security challenges, the paper identifies best practices and architectural strategies critical for real-world implementations. The discussion culminates in practical implications for industry adoption across sectors such as healthcare, finance, and e-commerce, and highlights future research directions including the development of standardized identity frameworks, AI integration, and decentralized identity systems in multi-cloud and edge computing environments. This study offers a comprehensive synthesis of current trends and technologies that are shaping the next generation of identity management in scalable digital platforms.
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Paventhan, A., Kenji Takeda, Simon J. Cox, and Denis A. Nicole. "Federated Database Services for Wind Tunnel Experiment Workflows." Scientific Programming 14, no. 3-4 (2006): 173–84. http://dx.doi.org/10.1155/2006/729069.
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Enabling the full life cycle of scientific and engineering workflows requires robust middleware and services that support effective data management, near-realtime data movement and custom data processing. Many existing solutions exploit the database as a passive metadata catalog. In this paper, we present an approach that makes use of federation of databases to host data-centric wind tunnel application workflows. The user is able to compose customized application workflows based on database services. We provide a reference implementation that leverages typical business tools and technologies: Microsoft SQL Server for database services and Windows Workflow Foundation for workflow services. The application data and user's code are both hosted in federated databases. With the growing interest in XML Web Services in scientific Grids, and with databases beginning to support native XML types and XML Web services, we can expect the role of databases in scientific computation to grow in importance.
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Jo, Jinyong, Yeonghun Chae, Heejin Jang, and JongUk Kong. "Federated-Access Management System and Videoconferencing Applications: Results from a Pilot Service during COVID-19 Pandemic." Electronics 10, no. 18 (2021): 2239. http://dx.doi.org/10.3390/electronics10182239.
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Videoconferencing has become a crucial enabler for sustainable collaboration and learning during the COVID-19 pandemic. However, national regulations often restrict public institutions from introducing commercial videoconferencing services. Open-source software is an attractive option for institutions if it can be protected from potential security threats while ensuring high usability. Unfortunately, to the best of our knowledge, we hardly find available open-source videoconferencing applications in the literature that stress their usability and adopt security-related frameworks. This study presents a federated-access management system called trustHub, which was developed to enable flexible and elaborate access control and protocol-agnostic user authentication. In addition, we introduce two videoconferencing applications that aim to improve the usability of leveraged open-source software. They are prototyped to operate in concert with trustHub to take firm access control and accept various types of identity providers. Consequently, using data collected from trustHub and a prototyped videoconferencing application over a 10-month period, we conduct a comprehensive analysis to understand the usage patterns of federated access and videoconferencing during the pandemic and, thus, verify their feasibility indirectly.
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Meshram, Atharva. "Growsight: A Comprehensive CRM and Business Insight Platform for Enhanced Decision Making." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 2135–39. http://dx.doi.org/10.22214/ijraset.2024.60259.
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Abstract: In the contemporary business environment, the fusion of Customer Relationship Management (CRM) and advanced Business Insights is vital for informed decision-making. This paper presents GrowSight, a cutting-edge CRM and Business Insights platform tailored for sales teams, marketers, CEOs, analysts, and freelancers. Leveraging the Flutter framework in Android Studio and the DhiWise plugin for intuitive UI design, GrowSight ensures a seamless user experience. Key to GrowSight's functionality is its integration of Firebase Google login, providing secure user onboarding. Each user receives a unique Global User ID (GUID) for data integrity. GrowSight supports CSV data maintenance and storage in Salesforce via REST APIs, enabling Business Insights with uploaded CSV files.
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Kolekar, Vikas K., and Sachin R. Sakhare. "A Research Perspective on Data Management Techniques for Federated Cloud Environment." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 5 (2023): 338–46. http://dx.doi.org/10.17762/ijritcc.v11i5.6622.
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Cloud computing has given a large scope of improvement in processing, storage and retrieval of data that is generated in huge amount from devices and users. Heterogenous devices and users generates the multidisciplinary data that needs to take care for easy and efficient storage and fast retrieval by maintaining quality and service level agreements. By just storing the data in cloud will not full fill the user requirements, the data management techniques has to be applied so that data adaptiveness and proactiveness characteristics are upheld. To manage the effectiveness of entire eco system a middleware must be there in between users and cloud service providers. Middleware has set of events and trigger based policies that will act on generated data to intermediate users and cloud service providers. For cloud service providers to deliver an efficient utilization of resources is one of the major issues and has scope of improvement in the federation of cloud service providers to fulfill user’s dynamic demands. Along with providing adaptiveness of data management in the middleware layer is challenging. In this paper, the policies of middleware for adaptive data management have been reviewed extensively. The main objectives of middleware are also discussed to accomplish high throughput of cloud service providers by means of federation and qualitative data management by means of adaptiveness and proactiveness. The cloud federation techniques have been studied thoroughly along with the pros and cons of it. Also, the strategies to do management of data has been exponentially explored.
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Fitri, Donna Ramadhan, Sepryta Handayani, Ferdi Yufriadi, Mona Eliza, and Abdullah A. Afifi. "Penerapan Sistem Absensi ID Card RFID Terhadap Perhitungan Honorarium, Kedisiplinan Pegawai dan Peningkatan Kualitas di Perguruan Darulfunun El-Abbasiyah." Journal of Regional Development and Technology Initiatives 2 (January 20, 2024): 1–11. http://dx.doi.org/10.58764/j.jrdti.2024.2.48.
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This research discusses the implementation of the RFID ID Card attendance system in the context of salary management and teacher and employee discipline at Darulfunun El-Abbasiyah College. The main objective of this research is to identify how the application of RFID ID Card technology in an attendance system can influence salary management and the level of discipline among teachers and employees in an institution or organization. The research method used is a descriptive method with a case study approach, then analyzed qualitatively to describe the impact of implementing the RFID ID Card attendance system on salary management and discipline. The results of this research show that the implementation of the RFID ID Card attendance system has several significant impacts. First, in terms of salary management, this system allows more accurate salary calculations based on real absences, reduces calculation errors, and minimizes the potential for system abuse. Second, regarding discipline, this system tends to increase the discipline of teachers and employees because their attendance is recorded accurately. This can also reduce the tendency to be late or absent from work for no apparent reason. Although implementing an RFID ID Card attendance system provides various benefits, this research also identified several challenges. The main challenges include the need for adequate training for system users, regular maintenance of RFID ID Card technology, and the importance of maintaining the privacy and security of user data. Overall, this research concludes that implementing an RFID ID Card attendance system can have a positive impact on salary management, discipline of employees, and quality improvement. However, successful implementation depends on a good understanding of this technology, adequate training, and appropriate actions to overcome challenges that may arise.
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Guo, Yingya, Kai Huang, and Jianshan Chen. "WCL: Client Selection in Federated Learning with a Combination of Model Weight Divergence and Client Training Loss for Internet Traffic Classification." Wireless Communications and Mobile Computing 2021 (December 11, 2021): 1–10. http://dx.doi.org/10.1155/2021/3381998.
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Internet traffic classification (TC) is a critical technique in network management and is widely applied in various applications. In traditional TC problems, the edge devices need to send the raw traffic data to the server for centralized processing, which not only generates a lot of communication overhead but also leads to the privacy leakage and information security issues. Federated learning (FL) is a new distributed machine learning paradigm that allows multiple clients to train a global model collaboratively without raw traffic data sharing. The TC in a FL framework preserves the user privacy and data security by keeping the raw traffic data local. However, because of the different user behaviours and user preferences, traffic data heterogeneity emerges. The existing FL solutions introduce bias in model training by averaging the local model parameters from all heterogeneous clients, which degrades the classification accuracy of the learnt global classification model. To improve the classification accuracy in heterogeneous data environment, this paper proposes a novel client selection algorithm, namely, WCL, in federated paradigm based on a combination of model weight divergence and local model training loss. Extensive experiments on the public traffic dataset QUIC and ISCX have proved that the WCL algorithm obtains, compared to CMFL, superior performance in improving model accuracy and convergence speed on low heterogeneous traffic data and high heterogeneous traffic data, respectively.
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Hooshmand, Y., J. Resch, P. Wischnewski, and P. Patil. "From a Monolithic PLM Landscape to a Federated Domain and Data Mesh." Proceedings of the Design Society 2 (May 2022): 713–22. http://dx.doi.org/10.1017/pds.2022.73.
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AbstractProduct Lifecycle Management (PLM) is one of the most business-critical IT backbones of manufacturing companies. It often consists of numerous, rigidly interwoven monolithic applications and is seen as synonymous with costly maintenance, lack of extensibility, and poor scalability. This paper proposes an approach for transforming a monolithic PLM landscape into a federated Domain and Data Mesh. This enhances semantic interoperability and enables data-driven use cases by treating data as first-class citizens. User-centric PLM domains moreover help to increase productivity in the workplace.
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Liu, Yu Jun, Meng Cai, and Kun Chen. "The Wireless TCP Disconnection Management." Applied Mechanics and Materials 513-517 (February 2014): 1246–51. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.1246.
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Disconnection is the real one which influences the transmission of data and media in the wireless environment. Due to the influence on the transmission quality, the disconnections are divided into temporary disconnections and longtime disconnections. To solve the disconnections, Wireless TCP Disconnection Management was propounded, which concludes TCP Fast Reconnection Mechanism for the Temporary Disconnection and User-Defined Times of TFRMTD. First, TFRMTD mechanism in the wireless was discussed in detail, which implements the fast reconnection through giving every client a unique ID and catching the results at the server. Second, UDTT is introduced, which discusses how to judge the failing of TFRMTD on the disconnections and then conclude its a longtime disconnection.
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Wassan, Sobia, Beenish Suhail, Riaqa Mubeen, et al. "Gradient Boosting for Health IoT Federated Learning." Sustainability 14, no. 24 (2022): 16842. http://dx.doi.org/10.3390/su142416842.
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Federated learning preserves the privacy of user data through Machine Learning (ML). It enables the training of an ML model during this process. The Healthcare Internet of Things (HIoT) can be used for intelligent technology, remote detection, remote medical care, and remote monitoring. The databases of many medical institutes include a vast quantity of medical information. Nonetheless, based on its specific nature of health information, susceptibilities to private information, and since it cannot be pooled related to data islands, Federated Learning (FL) offers a solution as a shared collaborative artificial intelligence technology. However, FL addresses a series of security and privacy issues. An adaptive Differential Security Federated Learning Healthcare IoT (DPFL-HIoT) model is proposed in this study. We propose differential privacy federated learning with an adaptive GBTM model algorithm for local updates, which helps adapt the model’s parameters based on the data characteristics and gradients. By training and applying a Gradient Boosted Trees model, the GBTM model identifies medical fraud based on patient information. This model is validated to check performance. Real-world experiments show that our proposed algorithm effectively protects data privacy.
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Shakil Parvez, Muhammad, and SARKER TANVEER AHMED RUMEE. "Electronic Identify Management in Digital Service Delivery: Current State of Adoption Around the World." International Journal of Information Technology and Applied Sciences (IJITAS) 4, no. 1 (2022): 01–11. http://dx.doi.org/10.52502/ijitas.v4i1.235.
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Electronic Identity Management has become a key ingredient for electronic governance. Countries around the world are using state-of-the-art technologies to digitize their citizen service delivery process. Uniquely identifying citizens and electronically managing their authentication information is a must for trust and fairness. However, at the implementation level, various modes of electronic identity management are currently in practice, primarily: centralized, user-centric, and federated models. This paper presents an overall summary of the level and type of identity management strategies adopted by different governments. We believe, this information will be valuable to the policymakers and development strategies to plan, design, implement and update the current state of the art.
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I.V., Solovei, and Vorochek O.H. "Development of the Software System for Managing a Digital Pet ID." System technologies 4, no. 153 (2024): 47–57. http://dx.doi.org/10.34185/1562-9945-4-153-2024-06.
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Recent Research and Publications Analysis: The shift towards digital documentation, such as Ukraine's «Diia» for human records, has not yet been fully embraced in pet care, creating a significant service gap. Ukraine, with a high number of household pets, especially approximately 7.63 million cats, highlights the need for a comprehensive digital pet management system. Given the frequent cases of pets getting lost—with low recovery rates—a robust digital system is essential for improving these figures and enhancing pet safety. When designing software, it is important to choose the right technologies for both the web interface and the backend. Django is recommended for the backend because of its «batteries-included» architecture, which provides a comprehensive set of ready-to-use tools that facilitate rapid development and ensure a high level of security. For the inter-face, it is recommended to use JavaScript integrated with frameworks such as React, An-gular or Vue to create dynamic applications that improve the user experience with asyn-chronous requests, allowing the content of the page to be updated without reloading the page. This combination not only provides scalability and security, but also effectively meets the complex needs of web applications. Purpose of the Study. This study aims to develop a software system that facilitates the management of digital pet IDs, which will integrate medical records, vaccination his-tories, and detailed activity logs. This integration aims to streamline pet care, making it more efficient and significantly more convenient for pet owners. Main Material Presentation. The proposed system's architecture will include: – backend. Utilizing Django REST Framework for creating scalable, secure web APIs that handle data operations efficiently; – frontend. Employing JavaScript, HTML, and CSS to provide a responsive and in-teractive user experience. Key Features: – digital passports for pets, verifiable via QR codes; - detailed activity logs that track and display pet movements and behaviors; – comprehensive vaccination records accessible by both pet owners and veterinari-ans; – a lost pet bulletin board that uses geographical data to notify users of nearby lost or found pets. Conclusions.Technologies have been chosen and a software system has been devel-oped for effective management of digital pet IDs, integrating key functions for compre-hensive data management of pets. It utilizes modern technologies to ensure reliable data security, high scalability, and enhanced user interaction, making it a key achievement in the management of digital pet IDs.