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Relevant bibliographies by topics / Federated network

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Academic literature on the topic 'Federated network'

Author: Grafiati

Published: 25 January 2025

Last updated: 31 July 2025

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Journal articles on the topic "Federated network"

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Шубин, Б., Т. Максимюк, О. Яремко, Л. Фабрі та Д. Мрозек. "МОДЕЛЬ ІНТЕГРАЦІЇ ФЕДЕРАТИВНОГО НАВЧАННЯ В МЕРЕЖІ МОБІЛЬНОГО ЗВ’ЯЗКУ 5-ГО ПОКОЛІННЯ". Information and communication technologies, electronic engineering 2, № 1 (2022): 26–35. http://dx.doi.org/10.23939/ictee2022.01.026.

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This paper investigates the main advantages of using Federated Learning (FL) for sharing experiences between intelligent devices in the environment of 5th generation mobile communication networks. This approach makes it possible to build effective machine learning algorithms using confidential data, the loss of which may be undesirable or even dangerous for users. Therefore, for the tasks where the confidentiality of the data is required for processing and analysis, we suggest using Federated Learning (FL) approaches. In this case, all users' personal information will be processed locally on t

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Zhang, Kainan, Zhipeng Cai, and Daehee Seo. "Privacy-Preserving Federated Graph Neural Network Learning on Non-IID Graph Data." Wireless Communications and Mobile Computing 2023 (February 3, 2023): 1–13. http://dx.doi.org/10.1155/2023/8545101.

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Since the concept of federated learning (FL) was proposed by Google in 2017, many applications have been combined with FL technology due to its outstanding performance in data integration, computing performance, privacy protection, etc. However, most traditional federated learning-based applications focus on image processing and natural language processing with few achievements in graph neural networks due to the graph’s nonindependent identically distributed (IID) nature. Representation learning on graph-structured data generates graph embedding, which helps machines understand graphs effecti

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Hang, Yifei. "Federated learning-based neural network for hotel cancellation prediction." Applied and Computational Engineering 45, no. 1 (2024): 190–95. http://dx.doi.org/10.54254/2755-2721/45/20241092.

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Hotel reservations have become a prevalent choice for customers. However, cancellations of these reservations present a significant challenge for hotels, potentially resulting in financial losses and a decline in customer satisfaction. To address the issue of improper management of cancellations and minimize losses, machine learning can be employed to analyze and predict cancellations based on customer information. In cooperative scenarios where hotels collaborate to train a unified model, traditional algorithms that aggregate all data raise concerns about the protection of sensitive customer

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Yu, Yun William, and Griffin M. Weber. "Balancing Accuracy and Privacy in Federated Queries of Clinical Data Repositories: Algorithm Development and Validation." Journal of Medical Internet Research 22, no. 11 (2020): e18735. http://dx.doi.org/10.2196/18735.

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Background Over the past decade, the emergence of several large federated clinical data networks has enabled researchers to access data on millions of patients at dozens of health care organizations. Typically, queries are broadcast to each of the sites in the network, which then return aggregate counts of the number of matching patients. However, because patients can receive care from multiple sites in the network, simply adding the numbers frequently double counts patients. Various methods such as the use of trusted third parties or secure multiparty computation have been proposed to link pa

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Kostenko, Valery Alekseevich, and Alisa Evgenievna Selezneva. "Types of Attacks on Federated Neural Networks and Methods of Protection." Proceedings of the Institute for System Programming of the RAS 36, no. 1 (2024): 35–44. http://dx.doi.org/10.15514/ispras-2024-36(1)-3.

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Federated learning is a technology for privacy-preserving learning in distributed storage systems. This training allows you to create a general forecasting model, storing all the data in your storage systems. Several devices take part in training the general model, and each device has its own unique data on which the neural network is trained. The interaction of devices occurs only to adjust the weights of the general model. After which, the updated model is transmitted to all devices. Training on multiple devices creates many attack opportunities against this type of network. After training o

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Ma, Xiaoyu, and Lize Gu. "Research and Application of Generative-Adversarial-Network Attacks Defense Method Based on Federated Learning." Electronics 12, no. 4 (2023): 975. http://dx.doi.org/10.3390/electronics12040975.

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In recent years, Federated Learning has attracted much attention because it solves the problem of data silos in machine learning to a certain extent. However, many studies have shown that attacks based on Generative Adversarial Networks pose a great threat to Federated Learning. This paper proposes Defense-GAN, a defense method against Generative Adversarial Network attacks under Federated Learning. Under this method, the attacker cannot learn the real image data distribution. Each Federated Learning participant uses SHAP to explain the model and masks the pixel features that have a greater im

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Tian, Mengmeng. "An Contract Theory based Federated Learning Aggregation Algorithm in IoT Network." Journal of Physics: Conference Series 2258, no. 1 (2022): 012008. http://dx.doi.org/10.1088/1742-6596/2258/1/012008.

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Abstract Taking IoT devices as the edge nodes is one of the powerful way to offloading the federated task since IoT devices are closer to the data generation end. The aggregation efficiency of federated learning in the IoT environment is inefficiency since the server of federated learning can not know the data quality of heterogeneous IoT device. How to encourage IoT edge clients to participate in federated learning and maximize the aggregation effect of the global model is an important problem. This paper proposes a federated learning aggregation model based on contract theory incentive mecha

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Al-Tameemi, M., M. B. Hassan, and S. A. Abass. "Federated Learning (FL) – Overview." LETI Transactions on Electrical Engineering & Computer Science 17, no. 5 (2024): 74–82. http://dx.doi.org/10.32603/2071-8985-2024-17-5-74-82.

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Explores the fundamental aspects of federated learning (FL) in the context of intrusion detection systems (IDS) within Internet of Things (IoT) networks. Federated learning presents an innovative approach to training machine learning models on distributed devices, thereby minimizing the need to transmit sensitive data to central servers. We classify FL into horizontal, vertical, and federated transfer learning and examine their application in IDS systems. Additionally, we analyze the network structure of FL, encompassing centralized and decentralized FL. Based on the conducted review, it can b

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Rizzato, Matteo, Youssef Laarouchi, and Christophe Geissler. "Using Federated Learning for Collaborative Intrusion Detection Systems." Journal of Systemics, Cybernetics and Informatics 21, no. 3 (2023): 29–36. http://dx.doi.org/10.54808/jsci.21.03.29.

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Neural networks have become cutting edge machine learning models for detecting network attacks. Traditional implementations provide fast and accurate predictions, but require centralised storage of labelled historical data for training. This solution is not always suitable for real-world applications, where regulatory constraints and privacy concerns hamper the collection of sensitive data into a single server. Federated Learning has recently been proposed as a framework for training a centralised model without the need to share data between different providers. We use the CICIDS2017 dataset p

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Wang, Shuangzhong, and Ying Zhang. "Multi-Level Federated Network Based on Interpretable Indicators for Ship Rolling Bearing Fault Diagnosis." Journal of Marine Science and Engineering 10, no. 6 (2022): 743. http://dx.doi.org/10.3390/jmse10060743.

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The federated learning network requires all the connection weights to be shared among the server and clients during training which increases the risk of data leakage. Meanwhile, the traditional federated learning method has a poor diagnostic effect for non-independently identically distributed data. In order to address these issues, a multi-level federated network based on interpretable indicators was proposed in this manuscript. Firstly, an interpretable adaptive sparse deep network is constructed based on the interpretability principle. Secondly, the relevance map of the network is construct

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Dissertations / Theses on the topic "Federated network"

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Kulkarni, Shweta Samir. "SECURE MIDDLEWARE FOR FEDERATED NETWORK PERFORMANCE MONITORING." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1366333088.

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Maka, Stephan. "Design and Implementation of a Federated Social Network." Master's thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2011. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-75477.

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Felt, Aaron J. "Federated ground station network model and interface specification." Thesis, Monterey, California: Naval Postgraduate School, 2014. http://hdl.handle.net/10945/44558.

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Approved for public release; distribution is unlimited<br>This thesis solves the problem of a lack of a complete, simple ground station network interface standard. A federated satellite ground station network (FGN) model and computer interface are developed that extend the use of ground stations to external users across the Internet. This should allow for reuse of existing ground stations, reducing costs and complexity of space missions. An improved model describing FGNs is proposed that defines a hierarchy of the components of the network, allowing for scalability and unified interfaces, and

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Demirci, Turan. "Federated Simulation Of Network Performance Using Packet Flow Modeling." Phd thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/2/12611704/index.pdf.

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Federated approach for the distributed simulation of a network, is an alternative method that aims to combine existing simulation models and software together using a Run Time Infrastructure (RTI), rather than building the whole simulation from scratch. In this study, an approach that significantly reduces the inter-federate communication load in federated simulation of communication networks is proposed. Rather than communicating packet-level information among federates, characteristics of packet flows in individual federates are dynamically identified and communicated. Flow characterization

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Cetin, Burak. "Wireless Network Intrusion Detection and Analysis using Federated Learning." Youngstown State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1588778320687729.

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Xu, Ran. "Federated Sensor Network architectural design for the Internet of Things (IoT)." Thesis, Loughborough University, 2013. https://dspace.lboro.ac.uk/2134/13453.

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An information technology that can combine the physical world and virtual world is desired. The Internet of Things (IoT) is a concept system that uses Radio Frequency Identification (RFID), WSN and barcode scanners to sense and to detect physical objects and events. This information is shared with people on the Internet. With the announcement of the Smarter Planet concept by IBM, the problem of how to share this data was raised. However, the original design of WSN aims to provide environment monitoring and control within a small scale local network. It cannot meet the demands of the IoT becaus

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Lu, Zonghao. "A case study about different network architectures in Federated Machine Learning." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-425193.

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Modern artificial intelligence (AI) technology is developing rapidly in recent years. Data is an important factor driving the development of AI.With the development of mobile Internet, more and more data is generated in different fields every day along with data sensitivity issues. As asignificant part of personal privacy, personal data must be respected and protected. Federated learning (FL) is a machine learning technology that can protect privacy because it keeps everyone’s data local. Many types of research have already confirmed that the bottleneck of federated learning is the communicati

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Grativol, Ribeiro Lucas. "Neural network compression in the context of federated learning and edge devices." Electronic Thesis or Diss., Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2024. http://www.theses.fr/2024IMTA0444.

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L’apprentissage fédéré est un cadre d’apprentissage automatique collaboratif et décentralisé, motivé par des préoccupations croissantes concernant la confidentialité desdonnées. En transférant l’entraînement des modèles vers des noeuds locaux et en conservant les données sur place, il favorise une approche plus respectueuse de la vie privée. Toutefois, cette méthode impose un surcoût en termes de communication et de calcul à ceux qui l’adoptent. Dans ce manuscrit, nous examinons les principaux défis de l’apprentissage fédéré et proposons des solutions visant à augumenter l’efficacité tout en r

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Sani, Lorenzo. "Unsupervised clustering of MDS data using federated learning." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amslaurea.unibo.it/25591/.

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In this master thesis we developed a model for unsupervised clustering on a data set of biomedical data. This data has been collected by GenoMed4All consortium from patients affected by Myelodysplastic Syndrome (MDS), that is an haematological disease. The main focus is put on the genetic mutations collected that are used as features of the patients in order to cluster them. Clustering approaches have been used in several studies concerning haematological diseases such MDS. A neural network-based model was used to solve the task. The results of the clustering have been compared with labels fro

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Gopalakrishnan, Aravind. "Network and Middleware Security for Enterprise Network Monitoring." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1339742304.

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Books on the topic "Federated network"

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Hong, Choong Seon, Latif U. Khan, Mingzhe Chen, Dawei Chen, Walid Saad, and Zhu Han. Federated Learning for Wireless Networks. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4963-9.

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Program, Yap Community Action. A rapid ecological assessment to inform the establishment of a network of marine protected areas for biodiversity and fisheries conservation for Yap State, Federated States of Micronesia. Yap Community Action Program, 2008.

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Lim, Wei Yang Bryan, Jer Shyuan Ng, Zehui Xiong, Dusit Niyato, and Chunyan Miao. Federated Learning Over Wireless Edge Networks. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-07838-5.

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Chen, Mingzhe, and Shuguang Cui. Communication Efficient Federated Learning for Wireless Networks. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-51266-7.

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1931-, Steinmann Heinrich, ed. Solutions for networked databases: How to move from heterogeneous structures to federated concepts. Academic Press, 1993.

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International Business Machines Corporation. International Technical Support Organization, ed. Certification Study Guide Series: IBM Tivoli Federated Identity Manager 6.1. IBM, International Technical Support Organization, 2009.

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International Workshop on Engineering Federated Information Systems (4th 2001 Berlin, Germany). Engineering federated information systems: Proceedings of the 4th workshop, EFIS 2001, Oct 9-10, 2001, Berlin (Germany). Akademische Verlagsgesellschaft Aka, 2001.

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International Workshop on Engineering Federated Information Systems (3rd 2000 Dublin, Ireland). Engineering federated information systems: Proceedings of the 3rd workshop, EFIS 2000, June 19-20, 2000, Dublin (Ireland). IOS Press, 2000.

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Dowling, Jim. Distributed Applications and Interoperable Systems: 13th IFIP WG 6.1 International Conference, DAIS 2013, Held as Part of the 8th International Federated Conference on Distributed Computing Techniques, DisCoTec 2013, Florence, Italy, June 3-5, 2013. Proceedings. Springer Berlin Heidelberg, 2013.

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Nicola, Rocco. Coordination Models and Languages: 15th International Conference, COORDINATION 2013, Held as Part of the 8th International Federated Conference on Distributed Computing Techniques, DisCoTec 2013, Florence, Italy, June 3-5, 2013. Proceedings. Springer Berlin Heidelberg, 2013.

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Book chapters on the topic "Federated network"

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Rodrigues, Thiago Gomes, Patricia Takako Endo, David W. S. C. Beserra, Djamel Sadok, and Judith Kelner. "Accountability for Federated Clouds." In Computer and Network Security Essentials. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58424-9_33.

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Bhoj, P., D. Caswell, S. Chutani, G. Gopal, and M. Kosarchyn. "Management of New Federated Services." In Integrated Network Management V. Springer US, 1997. http://dx.doi.org/10.1007/978-0-387-35180-3_25.

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Prabhugaonkar, Gargi Gopalkrishna, Xiaoyan Sun, Xuyu Wang, and Jun Dai. "Deep IoT Monitoring: Filtering IoT Traffic Using Deep Learning." In Silicon Valley Cybersecurity Conference. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-24049-2_8.

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AbstractThe use of IoT devices has significantly increased in recent years, but there have been growing concerns about the security and privacy issues associated with these IoT devices. A recent trend is to use deep network models to classify attack and benign traffic. A traditional approach is to train the models using centrally stored data collected from all the devices in the network. However, this framework raises concerns around data privacy and security. Attacks on the central server can compromise the data and expose sensitive information. To address the issues of data privacy and secur

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Choraś, Michał, Rafał Kozik, Rafał Piotrowski, Juliusz Brzostek, and Witold Hołubowicz. "Network Events Correlation for Federated Networks Protection System." In Towards a Service-Based Internet. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24755-2_9.

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Kong, Lingwei, Hengtao Tao, Jianzong Wang, Zhangcheng Huang, and Jing Xiao. "Network Coding for Federated Learning Systems." In Neural Information Processing. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63833-7_46.

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Peratikou, Adamantini, Constantinos Louca, Stavros Shiaeles, and Stavros Stavrou. "On Federated Cyber Range Network Interconnection." In Selected Papers from the 12th International Networking Conference. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-64758-2_9.

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Dhiyanesh, B., G. Kiruthiga, P. Saraswathi, S. Gomathi, and R. Radha. "Federated Learning for Efficient Cardiac Disease Prediction based on Hyper Spectral Feature Selection using Deep Spectral Convolution Neural Network." In Handbook on Federated Learning. CRC Press, 2023. http://dx.doi.org/10.1201/9781003384854-11.

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Gu, Shiqiao, Liu Yang, Siqi Deng, and Zhengyi Xu. "Two-Stream Communication-Efficient Federated Pruning Network." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-20868-3_14.

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Idoje, Godwin, Tasos Dagiuklas, and Muddesar Iqbal. "On the Performance of Federated Learning Network." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-54531-3_3.

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Takhellambam, Linthoingambi, Urikhimbam Boby Clinton, Yambem Ranjan Singh, Chinglensana Chandam, Khumukcham Robindro Singh, and Nazrul Hoque. "IoT Network Intrusion Detection Using Federated Learning." In Proceedings of the NIELIT's International Conference on Communication, Electronics and Digital Technology. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-3601-0_23.

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Conference papers on the topic "Federated network"

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Gao, Shangqian, Junyi Li, Zeyu Zhang, Yanfu Zhang, Weidong Cai, and Heng Huang. "Device-Wise Federated Network Pruning." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.01173.

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Gao, Tianli, Jiahong Lin, Congduan Li, Chee Wei Tan, and Jun Gao. "Federated Learning Meets Network Coding: Efficient Coded Hierarchical Federated Learning." In 2024 IEEE Information Theory Workshop (ITW). IEEE, 2024. https://doi.org/10.1109/itw61385.2024.10806940.

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Nguyen, Minh Hieu, Huu Tien Nguyen, Trung Thanh Nguyen, et al. "FedCert: Federated Accuracy Certification." In 2024 22nd International Symposium on Network Computing and Applications (NCA). IEEE, 2024. https://doi.org/10.1109/nca61908.2024.00045.

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Behera, Sadananda, Saroj Kumar Panda, Tania Panayiotou, and Georgios Ellinas. "Federated Learning for Network Traffic Prediction." In 2024 IFIP Networking Conference (IFIP Networking). IEEE, 2024. http://dx.doi.org/10.23919/ifipnetworking62109.2024.10619909.

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Mu, Xianyu, Youliang Tian, Zhou Zhou, and Jinbo Xiong. "Lightweight Federated Learning Secure Aggregation Protocols." In 2024 International Conference on Networking and Network Applications (NaNA). IEEE, 2024. http://dx.doi.org/10.1109/nana63151.2024.00079.

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Wu, Chen, Sencun Zhu, Prasenjit Mitra, and Wei Wang. "Unlearning Backdoor Attacks in Federated Learning." In 2024 IEEE Conference on Communications and Network Security (CNS). IEEE, 2024. http://dx.doi.org/10.1109/cns62487.2024.10735680.

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Busnel, Yann, and Léo Lavaur. "Tutorial: Federated Learning × Security for Network Monitoring." In 2024 IEEE 44th International Conference on Distributed Computing Systems Workshops (ICDCSW). IEEE, 2024. http://dx.doi.org/10.1109/icdcsw63686.2024.00008.

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Blakely, Benjamin, Shaurya Purohit, Kalvin Ogbuefi, Indelisio Prieto, and Manimaran Govindarasu. "A Federated Intrusion Response Network for Grids." In IECON 2024 - 50th Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2024. https://doi.org/10.1109/iecon55916.2024.10905821.

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Mohammadi, Keyhan, and Reza Ebrahimi Atani. "Sigma: A Secure Federated Network Gaming Platform." In 2024 15th International Conference on Information and Knowledge Technology (IKT). IEEE, 2024. https://doi.org/10.1109/ikt65497.2024.10892800.

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Singh, Jagdeep, Sanjay Kumar Dhurandher, and Isaac Woungang. "Federated Learning Empowered Routing for Opportunistic Network Environments." In 2024 IEEE International Conference on Communications Workshops (ICC Workshops). IEEE, 2024. http://dx.doi.org/10.1109/iccworkshops59551.2024.10615288.

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Reports on the topic "Federated network"

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Martinez, Richard G., Steven Polliard, and Robert Flo. Distributed Training Network Guard Trusted Bridge Federate Initial Capabilities Demonstration: After Action Report. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada408651.

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Pasupuleti, Murali Krishna. Next-Generation Extended Reality (XR): A Unified Framework for Integrating AR, VR, and AI-driven Immersive Technologies. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv325.

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Abstract: Extended Reality (XR), encompassing Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR), is evolving into a transformative technology with applications in healthcare, education, industrial training, smart cities, and entertainment. This research presents a unified framework integrating AI-driven XR technologies with computer vision, deep learning, cloud computing, and 5G connectivity to enhance immersion, interactivity, and scalability. AI-powered neural rendering, real-time physics simulation, spatial computing, and gesture recognition enable more realistic and adap

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African Open Science Platform Part 1: Landscape Study. Academy of Science of South Africa (ASSAf), 2019. http://dx.doi.org/10.17159/assaf.2019/0047.

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This report maps the African landscape of Open Science – with a focus on Open Data as a sub-set of Open Science. Data to inform the landscape study were collected through a variety of methods, including surveys, desk research, engagement with a community of practice, networking with stakeholders, participation in conferences, case study presentations, and workshops hosted. Although the majority of African countries (35 of 54) demonstrates commitment to science through its investment in research and development (R&D), academies of science, ministries of science and technology, policies, rec

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