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Articles de revues sur le sujet « Federated Management Architecture (Computer architecture) »

Auteur : Grafiati

Publié le 4 juin 2021

Mis à jour le 28 juillet 2025

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1

Aktas, Mehmet S., Geoffrey C. Fox, and Marlon Pierce. "A Federated Approach to Information Management in Grids." International Journal of Web Services Research 7, no. 1 (2010): 65–98. http://dx.doi.org/10.4018/jwsr.2010010104.

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We propose a novel approach to managing information in grids. The proposed approach is an add-on information system that provides unification and federation of grid information services. The system interacts with local information services and assembles their metadata instances under one hybrid architecture to provide a common query/publish interface to different kinds of metadata. The system also supports interoperability of major grid information services by providing federated information management. We present the semantics and architectural design for this system. We introduce a prototype

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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 sco

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Javed, Asad, Jérémy Robert, Keijo Heljanko, and Kary Främling. "IoTEF: A Federated Edge-Cloud Architecture for Fault-Tolerant IoT Applications." Journal of Grid Computing 18, no. 1 (2020): 57–80. http://dx.doi.org/10.1007/s10723-019-09498-8.

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AbstractThe evolution of Internet of Things (IoT) technology has led to an increased emphasis on edge computing for Cyber-Physical Systems (CPS), in which applications rely on processing data closer to the data sources, and sharing the results across heterogeneous clusters. This has simplified the data exchanges between IoT/CPS systems, the cloud, and the edge for managing low latency, minimal bandwidth, and fault-tolerant applications. Nonetheless, many of these applications administer data collection on the edge and offer data analytic and storage capabilities in the cloud. This raises the p

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Yousuf, Omerah, and Roohie Naaz Mir. "A survey on the Internet of Things security." Information & Computer Security 27, no. 2 (2019): 292–323. http://dx.doi.org/10.1108/ics-07-2018-0084.

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Purpose Internet of Things (IoT) is a challenging and promising system concept and requires new types of architectures and protocols compared to traditional networks. Security is an extremely critical issue for IoT that needs to be addressed efficiently. Heterogeneity being an inherent characteristic of IoT gives rise to many security issues that need to be addressed from the perspective of new architectures such as software defined networking, cryptographic algorithms, federated cloud and edge computing. Design/methodology/approach The paper analyzes the IoT security from three perspectives:

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Tam, Prohim, Riccardo Corrado, Chanthol Eang, and Seokhoon Kim. "Applicability of Deep Reinforcement Learning for Efficient Federated Learning in Massive IoT Communications." Applied Sciences 13, no. 5 (2023): 3083. http://dx.doi.org/10.3390/app13053083.

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To build intelligent model learning in conventional architecture, the local data are required to be transmitted toward the cloud server, which causes heavy backhaul congestion, leakage of personalization, and insufficient use of network resources. To address these issues, federated learning (FL) is introduced by offering a systematical framework that converges the distributed modeling process between local participants and the parameter server. However, the challenging issues of insufficient participant scheduling, aggregation policies, model offloading, and resource management still remain wi

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Jian, Weitao, Junshu He, Jiatao Chen, and Ming Cai. "A Privacy-Preserving and Edge-Collaborating Architecture for Personalized Mobility." Journal of Advanced Transportation 2023 (June 30, 2023): 1–16. http://dx.doi.org/10.1155/2023/8333560.

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Driven by technologies and demands, the modern transportation system has developed from intelligent transportation systems (ITS) to autonomous transportation systems (ATS) to resolve intertwined demands and supplies with few human interventions. In ATS, personal mobility service (PMS) is the service that can sense real-time traffic conditions comprehensively, learn travelers’ preferences accurately, recommend multimodal travel options appropriately, and provide service responses timely to elevate the level of personalization and intelligence in smart mobility services. Since current PMS widely

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Karras, Aristeidis, Anastasios Giannaros, Leonidas Theodorakopoulos, et al. "FLIBD: A Federated Learning-Based IoT Big Data Management Approach for Privacy-Preserving over Apache Spark with FATE." Electronics 12, no. 22 (2023): 4633. http://dx.doi.org/10.3390/electronics12224633.

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In this study, we introduce FLIBD, a novel strategy for managing Internet of Things (IoT) Big Data, intricately designed to ensure privacy preservation across extensive system networks. By utilising Federated Learning (FL), Apache Spark, and Federated AI Technology Enabler (FATE), we skilfully investigated the complicated area of IoT data management while simultaneously reinforcing privacy across broad network configurations. Our FLIBD architecture was thoughtfully designed to safeguard data and model privacy through a synergistic integration of distributed model training and secure model cons

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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 implemen

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Vijayarangan, Kumaran, and Saravanakumar Velusamy. "Comparative Insights on Centralized and Individual Models Using Snowflake and Google Big Query." Asian Journal of Research in Computer Science 18, no. 7 (2025): 155–63. https://doi.org/10.9734/ajrcos/2025/v18i7726.

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Data sharing plays a vital role in today’s digital ecosystem, allowing businesses, governments, and individuals to exchange information on a massive scale. Cloud-native data platforms have significantly transformed traditional data management practices by introducing scale architectures, decoupled storage and compute models, cost-efficiency and resource governance. This paper investigates two distinct paradigms, (Centralized and Individual) data sharing models. Centralized architecture offers consolidated governance, multi-tenant scalability, and advanced analytics enablement, whereas individu

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Fertalj, Kresimir, Natasa Bozic-Hoic, and Hrvoje Jerkovic. "The integration of learning object repositories and learning management systems." Computer Science and Information Systems 7, no. 3 (2010): 387–407. http://dx.doi.org/10.2298/csis081127001f.

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The systems aimed for manipulating large number of courses and students are called Learning Management Systems (LMS). A LMS can have excellent performance implemented through advanced Web technologies but it is often accompanied by a poor or rarely used repository of institution?s educational content. It has still remained. Still remains a problem how to allow users of a LMS to easily modify and integrate the content from federated e-learning repositories into their courses. This article presents an analysis of present repository frameworks and projects. FEDORA (Flexible Extensible Digital Rep

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Xavier, Percival, Wentong Cai, and Bu-Sung Lee. "Workload management of cooperatively federated computing clusters." Journal of Supercomputing 36, no. 3 (2006): 309–22. http://dx.doi.org/10.1007/s11227-006-8300-7.

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Nagasruthi Kattula. "Balancing innovation and ethics in Cloud-Driven CRM and security solutions." Global Journal of Engineering and Technology Advances 23, no. 1 (2025): 028–40. https://doi.org/10.30574/gjeta.2025.23.1.0094.

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Cloud technology, particularly in Enterprise CRM Engineering and Security Platform Integration, is fundamentally transforming society across education, public safety, and creative industries. This transformation delivers unprecedented efficiency, security, and accessibility while raising critical ethical considerations. Educational institutions leverage cloud-based CRM platforms to revolutionize learning through multi-tenant deployments, microservices architecture, and sophisticated identity management. Law enforcement organizations utilize specialized government cloud infrastructures with imm

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Buyya, Rajkumar, and Rajiv Ranjan. "Special section: Federated resource management in grid and cloud computing systems." Future Generation Computer Systems 26, no. 8 (2010): 1189–91. http://dx.doi.org/10.1016/j.future.2010.06.003.

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D'Souza, Adriana, David Hetherington, Géza Kulcsár, Bryan Orozco, Aleksander Przybyło, and István Ráth. "Configuration Management of Sets of Links in a Federated Tool Environment." INCOSE International Symposium 34, no. 1 (2024): 2236–45. http://dx.doi.org/10.1002/iis2.13267.

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AbstractIn the era of document‐centric systems engineering, large organizations developing complex systems had practical methods of associating drawings, specifications, requirements and other information with each other. Mostly these evolved methods involved part numbers and drawing numbers, although sometimes the methods included mundane techniques such as storing drawings in specific drawers in specific filing cabinets. These manual methods were very labor intensive, did not handle changes gracefully, and were somewhat error prone. As computer software and modeling tools began to displace p

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Karakaya, Aykut, and Sedat Akleylek. "A novel IoT-based health and tactical analysis model with fog computing." PeerJ Computer Science 7 (February 3, 2021): e342. http://dx.doi.org/10.7717/peerj-cs.342.

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In sports competitions, depending on the conditions such as excitement, stress, fatigue, etc. during the match, negative situations such as disability or loss of life may occur for players and spectators. Therefore, it is extremely important to constantly check their health. In addition, some strategic analyzes are made during the match. According to the results of these analyzes, the technical team affects the course of the match. Effects can have positive and sometimes negative results. In this article, fog computing and an Internet of Things (IoT) based architecture are proposed to produce

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Brecko, Alexander, Erik Kajati, Jiri Koziorek, and Iveta Zolotova. "Federated Learning for Edge Computing: A Survey." Applied Sciences 12, no. 18 (2022): 9124. http://dx.doi.org/10.3390/app12189124.

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New technologies bring opportunities to deploy AI and machine learning to the edge of the network, allowing edge devices to train simple models that can then be deployed in practice. Federated learning (FL) is a distributed machine learning technique to create a global model by learning from multiple decentralized edge clients. Although FL methods offer several advantages, including scalability and data privacy, they also introduce some risks and drawbacks in terms of computational complexity in the case of heterogeneous devices. Internet of Things (IoT) devices may have limited computing reso

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Wang, Ge, Fangmin Xu, Hengsheng Zhang, and Chenglin Zhao. "Joint resource management for mobility supported federated learning in Internet of Vehicles." Future Generation Computer Systems 129 (April 2022): 199–211. http://dx.doi.org/10.1016/j.future.2021.11.020.

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Khorasani, Neda, Saeid Abrishami, Mehdi Feizi, Mahdi Abolfazli Esfahani, and Faeze Ramezani. "Resource management in the federated cloud environment using Cournot and Bertrand competitions." Future Generation Computer Systems 113 (December 2020): 391–406. http://dx.doi.org/10.1016/j.future.2020.07.010.

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Kertesz, A., G. Kecskemeti, M. Oriol, et al. "Enhancing Federated Cloud Management with an Integrated Service Monitoring Approach." Journal of Grid Computing 11, no. 4 (2013): 699–720. http://dx.doi.org/10.1007/s10723-013-9269-0.

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Catuogno, Luigi, and Clemente Galdi. "Achieving interoperability between federated identity management systems: A case of study." Journal of High Speed Networks 20, no. 4 (2014): 209–21. http://dx.doi.org/10.3233/jhs-140499.

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Ranjan, Rajiv, Aaron Harwood, and Rajkumar Buyya. "Coordinated load management in Peer-to-Peer coupled federated grid systems." Journal of Supercomputing 61, no. 2 (2010): 292–316. http://dx.doi.org/10.1007/s11227-010-0426-y.

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Beck, A., T. Ganslandt, M. Hummel, et al. "IT Infrastructure Components for Biobanking." Applied Clinical Informatics 01, no. 04 (2010): 419–29. http://dx.doi.org/10.4338/aci-2010-05-ra-0034.

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Summary Objective: Within translational research projects in the recent years large biobanks have been established, mostly supported by homegrown, proprietary software solutions. No general requirements for biobanking IT infrastructures have been published yet. This paper presents an exemplary biobanking IT architecture, a requirements specification for a biorepository management tool and exemplary illustrations of three major types of requirements. Methods: We have pursued a comprehensive literature review for biobanking IT solutions and established an interdisciplinary expert panel for creat

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Memon, Shiraz, Jensen Jens, Elbers Willem, Helmut Neukirchen, Matthias Book, and Morris Riedel. "Towards Federated Service Discovery and Identity Management in Collaborative Data and Compute Cloud Infrastructures." Journal of Grid Computing 16, no. 4 (2018): 663–81. http://dx.doi.org/10.1007/s10723-018-9445-3.

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Astillo, Philip Virgil, Daniel Gerbi Duguma, Hoonyong Park, Jiyoon Kim, Bonam Kim, and Ilsun You. "Federated intelligence of anomaly detection agent in IoTMD-enabled Diabetes Management Control System." Future Generation Computer Systems 128 (March 2022): 395–405. http://dx.doi.org/10.1016/j.future.2021.10.023.

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Bekkouche, Oussama, Faqir Zarrar Yousaf, Xi Li, and Tarik Taleb. "Management and Orchestration of Mobile Network Services over Federated Mobile Infrastructures." IEEE Network 35, no. 6 (2021): 178–85. http://dx.doi.org/10.1109/mnet.101.2000522.

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Márquez-Sánchez, Sergio, Jaime Calvo-Gallego, Aiman Erbad, et al. "Enhancing Building Energy Management: Adaptive Edge Computing for Optimized Efficiency and Inhabitant Comfort." Electronics 12, no. 19 (2023): 4179. http://dx.doi.org/10.3390/electronics12194179.

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Nowadays, in contemporary building and energy management systems (BEMSs), the predominant approach involves rule-based methodologies, typically employing supervised or unsupervised learning, to deliver energy-saving recommendations to building occupants. However, these BEMSs often suffer from a critical limitation—they are primarily trained on building energy data alone, disregarding crucial elements such as occupant comfort and preferences. This inherent lack of adaptability to occupants significantly hampers the effectiveness of energy-saving solutions. Moreover, the prevalent cloud-based na

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Alsulami, Hemaid, Suhail H. Serbaya, Emad H. Abualsauod, Asem Majed Othman, Ali Rizwan, and Asadullah Jalali. "A Federated Deep Learning Empowered Resource Management Method to Optimize 5G and 6G Quality of Services (QoS)." Wireless Communications and Mobile Computing 2022 (March 23, 2022): 1–9. http://dx.doi.org/10.1155/2022/1352985.

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The quality of service (QoS) in 5G/6G communication enormously depends upon the mobility and agility of the network architecture. An increase in the possible uses of 5G vehicular network simultaneously expands the scope of the network’s quality of service (QoS). To this end, a safety-critical real-time system has become one of the most demanding criteria for the vehicular network. Although different mathematical and computation methods have traditionally been used to optimize the allocation of resources, but the nonconvexity of optimization issues creates unique type of challenges. In recent y

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Ali, Muhammad Nadeem, Muhammad Imran, Muhammad Salah ud din, and Byung-Seo Kim. "Low Rate DDoS Detection Using Weighted Federated Learning in SDN Control Plane in IoT Network." Applied Sciences 13, no. 3 (2023): 1431. http://dx.doi.org/10.3390/app13031431.

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The Internet of things (IoT) has opened new dimensions of novel services and computing power for modern living standards by introducing innovative and smart solutions. Due to the extensive usage of these services, IoT has spanned numerous devices and communication entities, which makes the management of the network a complex challenge. Hence it is urgently needed to redefine the management of the IoT network. Software-defined networking (SDN) intrinsic programmability and centralization features simplify network management, facilitate network abstraction, ease network evolution, has the potent

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Cheng, Haoyuan, and Qian Ai. "A Cost Optimization Method Based on Adam Algorithm for Integrated Demand Response." Electronics 12, no. 23 (2023): 4731. http://dx.doi.org/10.3390/electronics12234731.

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Demand response has gradually evolved into integrated demand response (IDR) as energy integration technology develops in integrated energy systems (IESs). The IES has a large amount of data interaction and an increasing concern for users’ privacy protection. Based on the combined cooling, heating, and power model, our study established an IDR management model considering demand-side energy coupling, focusing on cost optimization. In terms of privacy protection in the IDR management process, an optimization method based on the Adam algorithm was proposed. Only nonsensitive data, such as gradien

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Rafique, Ansar, Dimitri Van Landuyt, and Wouter Joosen. "PERSIST: Policy-Based Data Management Middleware for Multi-Tenant SaaS Leveraging Federated Cloud Storage." Journal of Grid Computing 16, no. 2 (2018): 165–94. http://dx.doi.org/10.1007/s10723-018-9434-6.

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Fu, Xiuhua, Rongqun Peng, Wenhao Yuan, et al. "Federated Learning-Based Resource Management with Blockchain Trust Assurance in Smart IoT." Electronics 12, no. 4 (2023): 1034. http://dx.doi.org/10.3390/electronics12041034.

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Resource management is a key issue that needs to be addressed in the future smart Internet of Things (IoT). This paper focuses on a Federated Learning (FL)-based resource management mechanism in IoT. It incorporates blockchain technology to guarantee the security of the FL model parameters exchange. We propose an IoT resource management framework incorporating blockchain and federated learning technologies; then, a specific FL-based resource management with a blockchain trust assurance algorithm is given. We use a Support Vector Machine (SVM) classifier to detect malicious nodes in order to av

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Balasubramanian, Venkatraman, Moayad Aloqaily, Martin Reisslein, and Anna Scaglione. "Intelligent Resource Management at the Edge for Ubiquitous IoT: An SDN-Based Federated Learning Approach." IEEE Network 35, no. 5 (2021): 114–21. http://dx.doi.org/10.1109/mnet.011.2100121.

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Zhang, Yongjing, Zhouyang Zhang, Shan Ji, Shenqing Wang, and Shitao Huang. "Conditional Proxy Re-Encryption-Based Key Sharing Mechanism for Clustered Federated Learning." Electronics 13, no. 5 (2024): 848. http://dx.doi.org/10.3390/electronics13050848.

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The need of data owners for privacy protection has given rise to collaborative learning, and data-related issues heterogeneity faced by federated learning has further given rise to clustered federated learning; whereas the traditional privacy-preserving scheme of federated learning using homomorphic encryption alone fails to fulfill the privacy protection demands of clustered federated learning. To address these issues, this research provides an effective and safeguarded answer for sharing homomorphic encryption keys among clusters in clustered federated learning grounded in conditional repres

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Kollu, Venkatagurunatham Naidu, Vijayaraj Janarthanan, Muthulakshmi Karupusamy, and Manikandan Ramachandran. "Cloud-Based Smart Contract Analysis in FinTech Using IoT-Integrated Federated Learning in Intrusion Detection." Data 8, no. 5 (2023): 83. http://dx.doi.org/10.3390/data8050083.

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Data sharing is proposed because the issue of data islands hinders advancement of artificial intelligence technology in the 5G era. Sharing high-quality data has a direct impact on how well machine-learning models work, but there will always be misuse and leakage of data. The field of financial technology, or FinTech, has received a lot of attention and is growing quickly. This field has seen the introduction of new terms as a result of its ongoing expansion. One example of such terminology is “FinTech”. This term is used to describe a variety of procedures utilized frequently in the financial

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Blanco, L., E. Zeydan, S. Barrachina-Munoz, F. Rezazadeh, L. Vettori, and J. Mangues-Bafalluy. "A Novel Approach for Scalable and Sustainable 6G Networks." Ieee Open Journal Of The Communications Society 5 (January 1, 2024): 1673–92. https://doi.org/10.1109/OJCOMS.2024.3372426.

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Hierarchical, distributed, scalable and Artificial Intelligence (AI)-based management of a massive number of network slices in different domains with the goal of zero-touch management is a major challenge for 6G networks. In this paper, we first propose a new vision for distributed network management and orchestration based on existing standardization architectures. This vision aims to embed AI/Machine Learning (ML) into the AI/ML architectures of Standardization Development Organizations (SDOs) such as the 3rd Generation Partnership Project (3GPP), the European Telecommunications Standards In

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Ehsanfar, Abbas, and Paul T. Grogan. "Mechanism Design for Exchanging Resources in Federated Networks." Journal of Network and Systems Management 28, no. 1 (2019): 108–32. http://dx.doi.org/10.1007/s10922-019-09498-9.

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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 pr

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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

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Belmonte, Ó., M. Castañeda, D. Fernández, et al. "Federate resource management in a Distributed Virtual Environment." Future Generation Computer Systems 26, no. 3 (2010): 308–17. http://dx.doi.org/10.1016/j.future.2009.08.014.

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Vassio, Luca, Danilo Giordano, Jinoh Kim, and Jon Crowcroft. "Message from the organizers ofWAIN." ACM SIGMETRICS Performance Evaluation Review 49, no. 4 (2022): 92. http://dx.doi.org/10.1145/3543146.3543167.

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We are pleased towelcome you to the 3rd InternationalWorkshop on AI in Networks and Distributed Systems (WAIN). The workshop aims to present high-quality researches leveraging machine learning and data analysis solutions to take full advantage of network systems and infrastructures. In detail, this year WAIN presents innovations for data center scheduling and management and network monitoring and security, using, among the others, node embedding techniques, GNN and federated learning. This year, our technical program is rich and varied, with 1 keynote speech and 6 accepted papers thoroughly ev

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Ehsanfar, Abbas, and Paul T. Grogan. "Auction-Based Algorithms for Routing and Task Scheduling in Federated Networks." Journal of Network and Systems Management 28, no. 2 (2019): 271–97. http://dx.doi.org/10.1007/s10922-019-09506-y.

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Skianis, Konstantinos, Anastasios Giannopoulos, Panagiotis Gkonis, and Panagiotis Trakadas. "Data Aging Matters: Federated Learning-Based Consumption Prediction in Smart Homes via Age-Based Model Weighting." Electronics 12, no. 14 (2023): 3054. http://dx.doi.org/10.3390/electronics12143054.

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Smart homes, powered mostly by Internet of Things (IoT) devices, have become very popular nowadays due to their ability to provide a holistic approach towards effective energy management. This is made feasible via the deployment of multiple sensors, which enables predicting energy consumption via machine learning approaches. In this work, we propose FedTime, a novel federated learning approach for predicting smart home consumption which takes into consideration the age of the time series datasets of each client. The proposed method is based on federated averaging but aggregates local models tr

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Liu, Yanbing, Hongbo Sun, Wenhui Fan, and Tianyuan Xiao. "A parallel matching algorithm based on order relation for HLA data distribution management." International Journal of Modeling, Simulation, and Scientific Computing 06, no. 02 (2015): 1540002. http://dx.doi.org/10.1142/s1793962315400024.

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In distribution simulation based on High-level architecture (HLA), data distribution management (DDM) is one of HLA services for the purpose of filtering the unnecessary data transferring over the network. DDM admits the sending federates and the receiving federates to express their interest using update regions and subscription regions in a multidimensional routing space. There are several matching algorithms to obtain overlap information between the update regions and subscription regions. When the number of regions increase sharply, the matching process is time consuming. However, the exist

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Čučko, Špela, Vid Keršič, and Muhamed Turkanović. "Towards a Catalogue of Self-Sovereign Identity Design Patterns." Applied Sciences 13, no. 9 (2023): 5395. http://dx.doi.org/10.3390/app13095395.

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Self-sovereign identity (SSI) is a user-centric, decentralised identity approach that provides a means for identification, authentication, and authorisation without the involvement of external entities, responsible for identity provisioning and management in current centralised and federated approaches. In general, the basic building blocks of an SSI system include decentralised identifiers, verifiable credentials, identity wallets, a verifiable data registry, and three main actors: issuer, identity holder, and verifier. Even though the SSI field is dominated by proposals, SSI systems can be i

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Alvi, Sohaib Bin Khalid, Muhammad Ziad Nayyer, Muhammad Hasan Jamal, et al. "A lightweight deep learning approach for COVID-19 detection using X-ray images with edge federation." DIGITAL HEALTH 9 (January 2023). http://dx.doi.org/10.1177/20552076231203604.

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Objective This study aims to develop a lightweight convolutional neural network-based edge federated learning architecture for COVID-19 detection using X-ray images, aiming to minimize computational cost, latency, and bandwidth requirements while preserving patient privacy. Method The proposed method uses an edge federated learning architecture to optimize task allocation and execution. Unlike in traditional edge networks where requests from fixed nodes are handled by nearby edge devices or remote clouds, the proposed model uses an intelligent broker within the federation to assess member edge

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Galal, Omar, Ahmed H. Abdel-Gawad, and Mona Farouk. "Federated Freeze BERT for text classification." Journal of Big Data 11, no. 1 (2024). http://dx.doi.org/10.1186/s40537-024-00885-x.

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AbstractPre-trained BERT models have demonstrated exceptional performance in the context of text classification tasks. Certain problem domains necessitate data distribution without data sharing. Federated Learning (FL) allows multiple clients to collectively train a global model by sharing learned models rather than raw data. However, the adoption of BERT, a large model, within a Federated Learning framework incurs substantial communication costs. To address this challenge, we propose a novel framework, FedFreezeBERT, for BERT-based text classification. FedFreezeBERT works by adding an aggrega

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Trindade, Silvana, Luiz F. Bittencourt, and Nelson L. S. da Fonseca. "Resource management at the network edge for federated learning." Digital Communications and Networks, October 2022. http://dx.doi.org/10.1016/j.dcan.2022.10.015.

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Merino, Jorge, Xiang Xie, Nicola Moretti, Janet Yoon Chang, and Ajith Parlikad. "Data Integration for Digital Twins in the built environment based on federated data models." Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction, September 21, 2023, 1–18. http://dx.doi.org/10.1680/jsmic.23.00002.

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Improving efficiency of operations is a major challenge in Facility Management given the limitations of outsourcing individual building functions to third-party companies. The status of each building function is isolated in siloes which are controlled by these third-party companies. Companies provide access to aggregated information in the form of reports through web portals, emails, or bureaucratic processes. Digital Twins represent an emerging approach to return awareness and control to facility managers by automating all levels of information access (from granular data to defined KPIs and r

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Dickinson, Matthew, Saptarshi Debroy, Prasad Calyam, et al. "Multi-cloud Performance and Security Driven Federated Workflow Management." IEEE Transactions on Cloud Computing, 2018, 1. http://dx.doi.org/10.1109/tcc.2018.2849699.

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Yang, Zhao, Shengbing Zhang, Chuxi Li, Miao Wang, Haoyang Wang, and Meng Zhang. "Efficient knowledge management for heterogenous federated continual learning on resource-constrained edge devices." Future Generation Computer Systems, February 2024. http://dx.doi.org/10.1016/j.future.2024.02.018.

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