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Letteratura scientifica selezionata sul tema "Federated network"

Autore: Grafiati

Pubblicato: 25 gennaio 2025

Ultima modifica: 19 luglio 2025

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Articoli di riviste sul tema "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 their devices. FL ensures the security of confidential data for subscribers, allows mobile network operators to reduce the amount of redundant information in the radio channel, and also allows optimizing the functioning of the mobile network. The paper presents a three-level model of integration of Federated Learning into the mobile network and describes the main features of this approach, as well as experimental studies that demonstrate the results of the proposed approach.

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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 effectively. Meanwhile, privacy protection plays a more meaningful role in analyzing graph-structured data such as social networks. Hence, this paper proposes PPFL-GNN, a novel privacy-preserving federated graph neural network framework for node representation learning, which is a pioneer work for graph neural network-based federated learning. In PPFL-GNN, clients utilize a local graph dataset to generate graph embeddings and integrate information from other collaborative clients to utilize federated learning to produce more accurate representation results. More importantly, by integrating embedding alignment techniques in PPFL-GNN, we overcome the obstacles of federated learning on non-IID graph data and can further reduce privacy exposure by sharing preferred information.

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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 information. In this context, federated learning emerges as an effective solution to ensure the privacy protection of customers while achieving the desired predictive outcomes. Thus, to protect customer privacy while preserving performance of the federated learning model in comparison to the non-federated version, this work proposed to implement both federated learning and non-federated algorithms based on neural network to make predictions of cancellation based on multiple factors. The federated learning approach achieved a final testing accuracy of 76.64%. Although this accuracy was about 9% lower than the non-federated case, its loss was over half the loss of non-federated, and its testing accuracy was similar to the training accuracy, while the non-federated algorithms testing accuracy was approximately 4% lower than the training one. Such results indicate that although accuracy was relatively lower, the federated learning approach prevented the overfitting problem in the non-federated case, while the data privacy problem was resolved.

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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 patient records across sites. However, they either have large trade-offs in accuracy and privacy or are not scalable to large networks. Objective This study aims to enable accurate estimates of the number of patients matching a federated query while providing strong guarantees on the amount of protected medical information revealed. Methods We introduce a novel probabilistic approach to running federated network queries. It combines an algorithm called HyperLogLog with obfuscation in the form of hashing, masking, and homomorphic encryption. It is tunable, in that it allows networks to balance accuracy versus privacy, and it is computationally efficient even for large networks. We built a user-friendly free open-source benchmarking platform to simulate federated queries in large hospital networks. Using this platform, we compare the accuracy, k-anonymity privacy risk (with k=10), and computational runtime of our algorithm with several existing techniques. Results In simulated queries matching 1 to 100 million patients in a 100-hospital network, our method was significantly more accurate than adding aggregate counts while maintaining k-anonymity. On average, it required a total of 12 kilobytes of data to be sent to the network hub and added only 5 milliseconds to the overall federated query runtime. This was orders of magnitude better than other approaches, which guaranteed the exact answer. Conclusions Using our method, it is possible to run highly accurate federated queries of clinical data repositories that both protect patient privacy and scale to large networks.

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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 on a local device, model data is sent via some type of communication to a central server or global model. Therefore, vulnerabilities in a federated network are possible not only at the training stage on a separate device, but also at the data exchange stage. All this together increases the number of possible vulnerabilities of federated neural networks. As is known, not only neural networks, but also other models can be used to build federated classifiers. Therefore, the types of attacks directly on the network also depend on the type of model used. Federated neural networks are a rather complex design, different from neural networks and other classifiers, which can be vulnerable to various types of attacks because training occurs on different devices, and both neural networks and simpler algorithms can be used. In addition, it is necessary to ensure data transfer between devices. All attacks come down to several main types that exploit classifier vulnerabilities. It is possible to implement protection against attacks by improving the architecture of the classifier itself and paying attention to data encryption.

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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 impact on classification and recognition in their respective image data. The experimental results show that while attacking the federated training model using masked images, the attacker cannot always obtain the ground truth of the images. At the same time, this paper also uses CutMix to improve the generalization ability of the model, and the obtained model accuracy is only 1% different from that of the model trained with the original data. The results show that the defense method proposed in this paper can not only resist Generative Adversarial Network attacks in Federated Learning and protect client privacy, but also ensure that the model accuracy of the Federated model will not be greatly affected.

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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 mechanism. Our experimental results show that the proposed algorithm effectively improves the aggregation efficiency of federated learning compared with FedAvg algorithm.

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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 be concluded that FL holds promise for enhancing data privacy and anomaly detection efficiency in IoT networks.

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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 provided by the Canadian Institute of Cybersecurity to demonstrate the benefits of Neural Networks-based Federated Learning for the detection of the most relevant types of network attacks. We conclude that a federated-trained neural network outperforms locally-trained models (at isoarchitecture) in terms of F1-score and False Negative detection ratio. Further, such model has a minor loss of performance and convergence rapidity compared to a model trained over a hypothetical centralised dataset.

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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 constructed based on interpretable indicators. Based on this map, the contribution of the connection weights in the network is used to build a multi-level federated network. Finally, the effectiveness of the proposed algorithm has been proved through experimental validation in the paper.

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Tesi sul tema "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 simplifying the process of using FGN resources. This model, which we call the Improved FGN model, is used to develop security schemes that are simple but effective. Simple but effective security schemes are then developed for this Improved FGN model, along with a standardized SOFtware interface. This interface connects external users to the network in order to extend ground station hardware to remote users as well as to simplify scheduling for the resource owners in a network. Different middleware frameworks are compared, and Apache Thrift is selected as the best fit for an FGN. This interface is then described and demonstrated with a reference implementation in Python. Recommendations for future improvements of this interface standard are discussed.

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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 is done with the Gaussian Mixtures Algorithm (GMA) using a Self Organizing Mixture Network (SOMN) technique. In simulations of a network partitioned into eight federates in space parallel manner, it is shown that significant speedups are achieved with the proposed approach without unduly compromising accuracy.

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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 because there is a lack of multi-connection functionality with other WSNs and upper level applications. As various standards of WSNs provide information for different purposes, a hybrid system that gives a complete answer by combining all of them could be promising for future IoT applications. This thesis is on the subject of `Federated Sensor Network' design and architectural development for the Internet of Things. A Federated Sensor Network (FSN) is a system that integrates WSNs and the Internet. Currently, methods of integrating WSNs and the Internet can follow one of three main directions: a Front-End Proxy solution, a Gateway solution or a TCP/IP Overlay solution. Architectures based on the ideas from all three directions are presented in this thesis; this forms a comprehensive body of research on possible Federated Sensor Network architecture designs. In addition, a fully compatible technology for the sensor network application, namely the Sensor Model Language (SensorML), has been reviewed and embedded into our FSN systems. The IoT as a new concept is also comprehensively described and the major technical issues discussed. Finally, a case study of the IoT in logistic management for emergency response is given. Proposed FSN architectures based on the Gateway solution are demonstrated through hardware implementation and lab tests. A demonstration of the 6LoWPAN enabled federated sensor network based on the TCP/IP Overlay solution presents a good result for the iNET localization and tracking project. All the tests of the designs have verified feasibility and achieve the target of the IoT concept.

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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 communication between the client and servers. Different ways of communication methods have various characteristics, resulting in differences inefficiency. We present a benchmark for our FL system using HTTP and gRPC communication protocol respectively to show that gRPC framework is faster and has better scalability than HTTP protocol mainly because of the different architectures and way of compacting message of these two different communication protocols. In addition, we found that the system may get crashed when the loads increased. A registration mechanism is proposed to deal with the problem of insufficient computing resources and apply a new model updatestrategy to make the training process finish in a shorter time.Tryckt av:

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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 réduisant les besoins en ressources matérielles. Plus précisément, nous explorons des techniques de compression classiques, comme l’élagage, ainsi que des approximations en rang faible afin de diminuer les coûts associés à l’apprentissage fédéré. Pour les scénarios où les participants disposent de capacités de communication limitées, nous introduisons une méthodologie de co-conception pour un algorithme d’apprentissage avec peu d’examples embarqué. Notre solution intègre les contraintes matérielles au sein d’un pipeline de déploiement sur des plateformes FPGA, aboutissant à un algorithme à faible latence qui peut également être exploité pour mettre en oeuvre des modèles post-apprentissage fédéré<br>Federated learning is a collaborative, decentralized machine learning framework driven by growing concerns about data privacy. By shifting model training to local nodes and keeping data local, it enables more privacy-conscious training. However, this approach imposes additional communication and computation overhead on those who adopt it. In this manuscript, we examine the key challenges in federated learning and propose solutions to increase efficiency and reduce hardware requirements. Specifically, we explore classic compression techniques, such as pruning, and low-rank approximations to lower the costs associated with federated learning. For scenarios where participants have limited communication capabilities, we introduce a co-design methodology for an embedded few-shot learning algorithm. Our proposed solution integrates hardware constraints into a deployment pipeline for FPGA platforms, resulting in a low-latency algorithm that can also be leveraged to implement post-federated learning models

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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 from a "gold standard'' technique, i.e. hierarchical Dirichlet processes (HDP). Our model was designed to be also implemented in the context of federated learning (FL). This innovative technique is able to achieve machine learning objective without the necessity of collecting all the data in one single center, allowing strict privacy policies to be respected. Federated learning was used because of its properties, and because of the sensitivity of data. Several recent studies regarding clinical problems addressed with machine learning endorse the development of federated learning settings in such context, because its privacy preserving properties could represent a cornerstone for applying machine learning techniques to medical data. In this work will be then discussed the clustering performance of the model, and also its generative capabilities.

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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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Libri sul tema "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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Capitoli di libri sul tema "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 security, federated learning is now a widely studied solution in the research community. In this paper, we explore and implement federated learning techniques to detect attack traffic in the IoT network. We use Deep Neural Networks on the labeled dataset and Autoencoder on the unlabeled dataset in a federated framework. We implement different model aggregation algorithms such as FedSGD, FedAvg, and FedProx for federated learning. We compare the performance of these federated learning models with the models in a centralized framework and study which aggregation algorithm for the global model yields the best performance for detecting attack traffic in the IoT network.

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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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5

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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8

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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Atti di convegni sul tema "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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Rapporti di organizzazioni sul tema "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 (sommario):

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 adaptive XR environments. Additionally, edge computing and federated learning enhance processing efficiency and privacy in decentralized XR applications, while blockchain and quantum-resistant cryptography secure transactions and digital assets in the metaverse. The study explores the role of AI-enhanced security, deepfake detection, and privacy-preserving AI techniques to mitigate risks associated with AI-driven XR. Case studies in healthcare, smart cities, industrial training, and gaming illustrate real-world applications and future research directions in neuromorphic computing, brain-computer interfaces (BCI), and ethical AI governance in immersive environments. This research lays the foundation for next-generation AI-integrated XR ecosystems, ensuring seamless, secure, and scalable digital experiences. Keywords: Extended Reality (XR), Augmented Reality (AR), Virtual Reality (VR), Mixed Reality (MR), Artificial Intelligence (AI), Neural Rendering, Spatial Computing, Deep Learning, 5G Networks, Cloud Computing, Edge Computing, Federated Learning, Blockchain, Cybersecurity, Brain-Computer Interfaces (BCI), Quantum Computing, Privacy-Preserving AI, Human-Computer Interaction, Metaverse.

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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, recognition of research, and participation in the Science Granting Councils Initiative (SGCI), the following countries demonstrate the highest commitment and political willingness to invest in science: Botswana, Ethiopia, Kenya, Senegal, South Africa, Tanzania, and Uganda. In addition to existing policies in Science, Technology and Innovation (STI), the following countries have made progress towards Open Data policies: Botswana, Kenya, Madagascar, Mauritius, South Africa and Uganda. Only two African countries (Kenya and South Africa) at this stage contribute 0.8% of its GDP (Gross Domestic Product) to R&D (Research and Development), which is the closest to the AU’s (African Union’s) suggested 1%. Countries such as Lesotho and Madagascar ranked as 0%, while the R&D expenditure for 24 African countries is unknown. In addition to this, science globally has become fully dependent on stable ICT (Information and Communication Technologies) infrastructure, which includes connectivity/bandwidth, high performance computing facilities and data services. This is especially applicable since countries globally are finding themselves in the midst of the 4th Industrial Revolution (4IR), which is not only “about” data, but which “is” data. According to an article1 by Alan Marcus (2015) (Senior Director, Head of Information Technology and Telecommunications Industries, World Economic Forum), “At its core, data represents a post-industrial opportunity. Its uses have unprecedented complexity, velocity and global reach. As digital communications become ubiquitous, data will rule in a world where nearly everyone and everything is connected in real time. That will require a highly reliable, secure and available infrastructure at its core, and innovation at the edge.” Every industry is affected as part of this revolution – also science. An important component of the digital transformation is “trust” – people must be able to trust that governments and all other industries (including the science sector), adequately handle and protect their data. This requires accountability on a global level, and digital industries must embrace the change and go for a higher standard of protection. “This will reassure consumers and citizens, benefitting the whole digital economy”, says Marcus. A stable and secure information and communication technologies (ICT) infrastructure – currently provided by the National Research and Education Networks (NRENs) – is key to advance collaboration in science. The AfricaConnect2 project (AfricaConnect (2012–2014) and AfricaConnect2 (2016–2018)) through establishing connectivity between National Research and Education Networks (NRENs), is planning to roll out AfricaConnect3 by the end of 2019. The concern however is that selected African governments (with the exception of a few countries such as South Africa, Mozambique, Ethiopia and others) have low awareness of the impact the Internet has today on all societal levels, how much ICT (and the 4th Industrial Revolution) have affected research, and the added value an NREN can bring to higher education and research in addressing the respective needs, which is far more complex than simply providing connectivity. Apart from more commitment and investment in R&D, African governments – to become and remain part of the 4th Industrial Revolution – have no option other than to acknowledge and commit to the role NRENs play in advancing science towards addressing the SDG (Sustainable Development Goals). For successful collaboration and direction, it is fundamental that policies within one country are aligned with one another. Alignment on continental level is crucial for the future Pan-African African Open Science Platform to be successful. Both the HIPSSA ((Harmonization of ICT Policies in Sub-Saharan Africa)3 project and WATRA (the West Africa Telecommunications Regulators Assembly)4, have made progress towards the regulation of the telecom sector, and in particular of bottlenecks which curb the development of competition among ISPs. A study under HIPSSA identified potential bottlenecks in access at an affordable price to the international capacity of submarine cables and suggested means and tools used by regulators to remedy them. Work on the recommended measures and making them operational continues in collaboration with WATRA. In addition to sufficient bandwidth and connectivity, high-performance computing facilities and services in support of data sharing are also required. The South African National Integrated Cyberinfrastructure System5 (NICIS) has made great progress in planning and setting up a cyberinfrastructure ecosystem in support of collaborative science and data sharing. The regional Southern African Development Community6 (SADC) Cyber-infrastructure Framework provides a valuable roadmap towards high-speed Internet, developing human capacity and skills in ICT technologies, high- performance computing and more. The following countries have been identified as having high-performance computing facilities, some as a result of the Square Kilometre Array7 (SKA) partnership: Botswana, Ghana, Kenya, Madagascar, Mozambique, Mauritius, Namibia, South Africa, Tunisia, and Zambia. More and more NRENs – especially the Level 6 NRENs 8 (Algeria, Egypt, Kenya, South Africa, and recently Zambia) – are exploring offering additional services; also in support of data sharing and transfer. The following NRENs already allow for running data-intensive applications and sharing of high-end computing assets, bio-modelling and computation on high-performance/ supercomputers: KENET (Kenya), TENET (South Africa), RENU (Uganda), ZAMREN (Zambia), EUN (Egypt) and ARN (Algeria). Fifteen higher education training institutions from eight African countries (Botswana, Benin, Kenya, Nigeria, Rwanda, South Africa, Sudan, and Tanzania) have been identified as offering formal courses on data science. In addition to formal degrees, a number of international short courses have been developed and free international online courses are also available as an option to build capacity and integrate as part of curricula. The small number of higher education or research intensive institutions offering data science is however insufficient, and there is a desperate need for more training in data science. The CODATA-RDA Schools of Research Data Science aim at addressing the continental need for foundational data skills across all disciplines, along with training conducted by The Carpentries 9 programme (specifically Data Carpentry 10 ). Thus far, CODATA-RDA schools in collaboration with AOSP, integrating content from Data Carpentry, were presented in Rwanda (in 2018), and during17-29 June 2019, in Ethiopia. Awareness regarding Open Science (including Open Data) is evident through the 12 Open Science-related Open Access/Open Data/Open Science declarations and agreements endorsed or signed by African governments; 200 Open Access journals from Africa registered on the Directory of Open Access Journals (DOAJ); 174 Open Access institutional research repositories registered on openDOAR (Directory of Open Access Repositories); 33 Open Access/Open Science policies registered on ROARMAP (Registry of Open Access Repository Mandates and Policies); 24 data repositories registered with the Registry of Data Repositories (re3data.org) (although the pilot project identified 66 research data repositories); and one data repository assigned the CoreTrustSeal. Although this is a start, far more needs to be done to align African data curation and research practices with global standards. Funding to conduct research remains a challenge. African researchers mostly fund their own research, and there are little incentives for them to make their research and accompanying data sets openly accessible. Funding and peer recognition, along with an enabling research environment conducive for research, are regarded as major incentives. The landscape report concludes with a number of concerns towards sharing research data openly, as well as challenges in terms of Open Data policy, ICT infrastructure supportive of data sharing, capacity building, lack of skills, and the need for incentives. Although great progress has been made in terms of Open Science and Open Data practices, more awareness needs to be created and further advocacy efforts are required for buy-in from African governments. A federated African Open Science Platform (AOSP) will not only encourage more collaboration among researchers in addressing the SDGs, but it will also benefit the many stakeholders identified as part of the pilot phase. The time is now, for governments in Africa, to acknowledge the important role of science in general, but specifically Open Science and Open Data, through developing and aligning the relevant policies, investing in an ICT infrastructure conducive for data sharing through committing funding to making NRENs financially sustainable, incentivising open research practices by scientists, and creating opportunities for more scientists and stakeholders across all disciplines to be trained in data management.

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