Relevant bibliographies by topics / Frameworks of FL

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  3. Books
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Academic literature on the topic 'Frameworks of FL'

Author: Grafiati
Published: 5 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Frameworks of FL"

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Byrnes,, Heidi. "Of frameworks and the goals of collegiate foreign language education: critical reflections." Applied Linguistics Review 3, no. 1 (2012): 1–24. http://dx.doi.org/10.1515/applirev-2012-0001.

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AbstractThe paper suggests that among reasons for the difficulties collegiate foreign language (FL) programs in the United States (and most likely elsewhere) encounter in assuring that their students attain the kind of upper-level multiple literacies necessary for engaging in sophisticated work with FL oral and written texts may be the fact that prevailing frameworks for capturing FL performance, development, and assessment are insufficient for envisioning such textually oriented learning goals. The result of this mismatch between dominant frameworks, typically associated with communicative la
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I., Venkata Dwaraka Srihith. "Federated Frameworks: Pioneering Secure and Decentralized Authentication Systems." Journal of Advances in Computational Intelligence Theory 7, no. 1 (2024): 31–40. https://doi.org/10.5281/zenodo.13968684.

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<em>Federated Learning (FL) is innovative machine learning approach that lets multiple devices work together to train models without sharing sensitive data. By keeping data on the device, FL not only boosts privacy and security but also helps improve models collectively. Recent research looked into how Blockchain technology could strengthen FL, tackling existing security issues. Blockchain adds a safeguard against threats like data tampering or unauthorized access and makes systems more transparent and fairer by improving how records and rewards are managed. By blending Blockchain with FL, we
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Rajendran, Suraj, Zhenxing Xu, Weishen Pan, Arnab Ghosh, and Fei Wang. "Data heterogeneity in federated learning with Electronic Health Records: Case studies of risk prediction for acute kidney injury and sepsis diseases in critical care." PLOS Digital Health 2, no. 3 (2023): e0000117. http://dx.doi.org/10.1371/journal.pdig.0000117.

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With the wider availability of healthcare data such as Electronic Health Records (EHR), more and more data-driven based approaches have been proposed to improve the quality-of-care delivery. Predictive modeling, which aims at building computational models for predicting clinical risk, is a popular research topic in healthcare analytics. However, concerns about privacy of healthcare data may hinder the development of effective predictive models that are generalizable because this often requires rich diverse data from multiple clinical institutions. Recently, federated learning (FL) has demonstr
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Gufran, Danish, and Sudeep Pasricha. "FedHIL: Heterogeneity Resilient Federated Learning for Robust Indoor Localization with Mobile Devices." ACM Transactions on Embedded Computing Systems 22, no. 5s (2023): 1–24. http://dx.doi.org/10.1145/3607919.

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Indoor localization plays a vital role in applications such as emergency response, warehouse management, and augmented reality experiences. By deploying machine learning (ML) based indoor localization frameworks on their mobile devices, users can localize themselves in a variety of indoor and subterranean environments. However, achieving accurate indoor localization can be challenging due to heterogeneity in the hardware and software stacks of mobile devices, which can result in inconsistent and inaccurate location estimates. Traditional ML models also heavily rely on initial training data, ma
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Mar’i, Farhanna, and Ahmad Afif Supianto. "A conceptual approach of optimization in federated learning." Indonesian Journal of Electrical Engineering and Computer Science 37, no. 1 (2025): 288. http://dx.doi.org/10.11591/ijeecs.v37.i1.pp288-299.

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Federated learning (FL) is an emerging approach to distributed learning from decentralized data, designed with privacy concerns in mind. FL has been successfully applied in several fields, such as the internet of things (IoT), human activity recognition (HAR), and natural language processing (NLP), showing remarkable results. However, the development of FL in real-world applications still faces several challenges. Recent optimizations of FL have been made to address these issues and enhance the FL settings. In this paper, we categorize the optimization of FL into five main challenges: Communic
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Farhanna, Mar'i Ahmad Afif Supianto. "A conceptual approach of optimization in federated learning." Indonesian Journal of Electrical Engineering and Computer Science 37, no. 1 (2025): 288–99. https://doi.org/10.11591/ijeecs.v37.i1.pp288-299.

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Federated learning (FL) is an emerging approach to distributed learning from decentralized data, designed with privacy concerns in mind. FL has been successfully applied in several fields, such as the internet of things (IoT), human activity recognition (HAR), and natural language processing (NLP), showing remarkable results. However, the development of FL in real-world applications still faces several challenges. Recent optimizations of FL have been made to address these issues and enhance the FL settings. In this paper, we categorize the optimization of FL into five main challenges: Communic
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Wang, Hanjing. "The practical applications of federated learning across various domains." Applied and Computational Engineering 87, no. 1 (2024): 154–61. http://dx.doi.org/10.54254/2755-2721/87/20241582.

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With the advancement of artificial intelligence technology, a vast amount of data is transmitted during the model training process, significantly increasing the risk of data leakage. In an era where data privacy is highly valued, protecting data from leakage has become an urgent issue. Federated Learning (FL) has thus been proposed and applied across various fields. This paper presents the applications of FL in five key areas: healthcare, urban transportation, computer vision, Industrial Internet of Things (IIoT), and 5G networks. This paper discusses the feasibility of implementing FL for pri
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Luay Bahjat Albtosh. "Harnessing the power of federated learning to advance technology." World Journal of Advanced Research and Reviews 23, no. 3 (2024): 1302–12. http://dx.doi.org/10.30574/wjarr.2024.23.3.2768.

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Federated Learning (FL) has emerged as a transformative paradigm in machine learning, advocating for decentralized, privacy-preserving model training. This study provides a comprehensive evaluation of contemporary FL frameworks – TensorFlow Federated (TFF), PySyft, and FedJAX – across three diverse datasets: CIFAR-10, IMDb reviews, and the UCI Heart Disease dataset. Our results demonstrate TFF's superior performance on image classification tasks, while PySyft excels in both efficiency and privacy for textual data. The study underscores the potential of FL in ensuring data privacy and model per
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Chia, Harmon Lee Bruce. "Harnessing the power of federated learning to advance technology." Advances in Engineering Innovation 2, no. 1 (2023): 44–47. http://dx.doi.org/10.54254/2977-3903/2/2023020.

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Federated Learning (FL) has emerged as a transformative paradigm in machine learning, advocating for decentralized, privacy-preserving model training. This study provides a comprehensive evaluation of contemporary FL frameworks TensorFlow Federated (TFF), PySyft, and FedJAX across three diverse datasets: CIFAR-10, IMDb reviews, and the UCI Heart Disease dataset. Our results demonstrate TFF's superior performance on image classification tasks, while PySyft excels in both efficiency and privacy for textual data. The study underscores the potential of FL in ensuring data privacy and model perform
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Luay, Bahjat Albtosh. "Harnessing the power of federated learning to advance technology." World Journal of Advanced Research and Reviews 23, no. 3 (2024): 1303–12. https://doi.org/10.5281/zenodo.14945174.

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Federated Learning (FL) has emerged as a transformative paradigm in machine learning, advocating for decentralized, privacy-preserving model training. This study provides a comprehensive evaluation of contemporary FL frameworks &ndash; TensorFlow Federated (TFF), PySyft, and FedJAX &ndash; across three diverse datasets: CIFAR-10, IMDb reviews, and the UCI Heart Disease dataset. Our results demonstrate TFF's superior performance on image classification tasks, while PySyft excels in both efficiency and privacy for textual data. The study underscores the potential of FL in ensuring data privacy a
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Dissertations / Theses on the topic "Frameworks of FL"

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Lazarus, Dayna J. "Making a Case for Equity Planning in Transportation Development: Identifying Indicators and Building a Framework for Hillsborough County, FL." Scholar Commons, 2019. https://scholarcommons.usf.edu/etd/7840.

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The idea that planners should work toward an equitable society has been part of the profession since the 1960s, largely based on the work of planning theorists like Paul Davidoff, Sherry Arnstein and Norman Krumholz. Transportation planning, however, has been slower than other sectors of the profession, such as housing, to embrace equity planning concepts. That has begun to change as concerns about income inequality, environmental justice and climate change have become more salient. This thesis makes the case that in order to improve social equity outcomes, transportation planners must make so
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Windridge, David, Michael Felsberg, and Affan Shaukat. "A Framework for Hierarchical Perception–Action Learning Utilizing Fuzzy Reasoning." Linköpings universitet, Datorseende, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-85688.

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Perception-action (P-A) learning is an approach to cognitive system building that seeks to reduce the complexity associated with conventional environment-representation/action-planning approaches. Instead, actions are directly mapped onto the perceptual transitions that they bring about, eliminating the need for intermediate representation and significantly reducing training requirements. We here set out a very general learning framework for cognitive systems in which online learning of the P-A mapping may be conducted within a symbolic processing context, so that complex contextual reasoning
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Wilder, Jessica A. "Operationalizing the Pressure and Release Theoretical Framework Using Risk Ratio Analysis to Measure Vulnerability and Predict Risk from Natural Hazards in the Tampa, FL Metropolitan Area." Scholar Commons, 2018. http://scholarcommons.usf.edu/etd/7245.

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Significant damage and loss is experienced every year due to natural hazards such as hurricanes, tornadoes, droughts, floods, wildfires, volcanoes, and earthquakes. NOAA’s National Center for Environmental Information (NCEI) reports that in 2016 the United States experienced more than a dozen climate disaster events with damages and loss in excess of a billion dollars (NOAA National Centers for Environmental Information, 2017). Identifying vulnerabilities and risk associated with disaster threats is now a major focus of natural hazards research. Natural hazards research has yielded numerou
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Gurram, Sashikanth. "Understanding the Linkages between Urban Transportation Design and Population Exposure to Traffic-Related Air Pollution: Application of an Integrated Transportation and Air Pollution Modeling Framework to Tampa, FL." Scholar Commons, 2017. http://scholarcommons.usf.edu/etd/7030.

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Rapid and unplanned urbanization has ushered in a variety of public health challenges, including exposure to traffic pollution and greater dependence on automobiles. Moreover, vulnerable population groups often bear the brunt of negative outcomes and are subject to disproportionate exposure and health effects. This makes it imperative for urban transportation engineers, land use planners, and public health professionals to work synergistically to understand both the relationship between urban design and population exposure to traffic pollution, and its social distribution
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Books on the topic "Frameworks of FL"

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Andronati, S. A. Managementul bazinului transfrontalier al fl. Nistru și Directiva-cadru a apelor a Uniunii Europene: Materialele conferinței internaționale, Chișinaŭ, 2-3 octombrie 2008 = Upravlenie basseĭnom transgranichnoĭ reki Dnestr i Vodnai︠a︡ ramochnai︠a︡ direktiva Evropeĭskogo Soi︠u︡za : materialy mezhdunarodnoĭ konferent︠s︡ii, Kishinev, 2-3 okti︠a︡bri︠a︡ 2008 g. = Transboundary Dniester River Basin management and the EU Water Framework Directive : proceedings of the international conference, Chișinaŭ, October 2-3, 2008. Eco-TIRAS, 2008.

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Book chapters on the topic "Frameworks of FL"

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Legler, Tatjana, Vinit Hegiste, and Martin Ruskowski. "Multifaceted Applications of Federated Learning: Beyond Neural Networks." In Lecture Notes in Mechanical Engineering. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-86489-6_28.

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Abstract The advent of Federated Learning (FL) has brought about a revolutionary change in the field of machine learning, enabling the decentralised training of models across a multitude of devices while simultaneously maintaining the confidentiality of the data. In contrast to conventional centralized methodologies, FL maintains the localisation of data, with only model updates being shared. This methodology enhances model generalisation and stability without compromising data sovereignty. A variety of machine learning techniques, including support vector machines (SVMs) and decision trees, c
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Zhao, Jia, Xinyu Rao, JiQiang Liu, Yue Guo, and BoKai Yang. "CVAR-FL IoV Intrusion Detection Framework." In Information Security Practice and Experience. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-7032-2_8.

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Hegiste, Vinit, Tatjana Legler, and Martin Ruskowski. "Collaborative Learning in Shared Production Environment Using Federated Image Classification." In Lecture Notes in Mechanical Engineering. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-86489-6_11.

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Abstract The application of federated learning (FL) in industrial settings offers promising advancements in maintaining data privacy while collaboratively training machine learning models. This study focuses on the comparative analysis of federated image classification versus locally trained models within a shared production environment. Specifically, we explore the classification of windshields in truck cabins, which is a crucial task for quality inspection in manufacturing of trucks. Our research involves four clients, each producing different types of truck cabins and research based on FL p
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Marzo, Stefano, Royston Pinto, Lucy McKenna, and Rob Brennan. "Privacy-Enhanced ZKP-Inspired Framework for Balanced Federated Learning." In Communications in Computer and Information Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26438-2_20.

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AbstractFederated learning (FL) is a distributed machine learning approach that enables remote devices i.e. workers to collaborate to compute the fitting of a neural network model without sharing their data. While this method is favorable to ensure data privacy, an imbalanced data distribution can introduce unfairness in the model training, causing discriminatory bias towards certain under-represented groups. In this paper, we show that imbalance federated data decreases indexes of equity i.e. differences in treatment for underrepresented classes. To address the problem, we propose a federated
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Wang, Xinyan, An Hu, Jingli Jia, Jiacheng Du, Yongjie Ning, and Ying Zhu. "Dual-Layer FL and Blockchain Empowered High Accurate Edge Training Framework." In Smart Innovation, Systems and Technologies. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-7161-9_21.

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do Nascimento, Francisco Assis Moreira, and Fabiano Hessel. "DCD-FL: A Decentralized Federated Learning Framework for Intrusion Detection in IoT." In Lecture Notes on Data Engineering and Communications Technologies. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-87769-8_4.

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Gupta, Rajesh, Nilesh Kumar Jadav, Sudeep Tanwar, and Anand Nayyar. "FL and Onion Routing-Based Secure EHR Exchange Framework for Smart Healthcare System." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-1188-1_25.

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Jiménez-Caicedo, Juan Pablo. "Uncovering Spanish Harlem: Ethnographic Linguistic Landscape Projects in an Advanced Content-Based Spanish Course." In Educational Linguistics. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-39578-9_6.

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AbstractLinguistic and cultural diversity are hallmarks of postmodern globalized societies. In New York city, for example, the massive influx of immigrants from the Caribbean, especially Puerto Ricans after 1917, altered the linguistic and cultural landscape of an urban center already known for its large concentration of foreign settlers. This chapter reports on a case study of an advanced Spanish course application of the linguistic landscape (LL) as a site for learning. Drawing on a literacy-oriented approach to Foreign Language (FL) education as a framework for integrating LL into an advanc
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Krishnamoorthy, P., and R. Menaka. "Application of Federated Learning Models for Privacy-Preserving Detection of Cyber Attacks in Cross-Domain Networks." In Integration of Machine Learning Models for Real-Time Detection of Advanced Persistent Threats and Network Intrusions. RADemics Research Institute, 2025. https://doi.org/10.71443/9789349552388-11.

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This book chapter explores the integration of Federated Learning (FL) with cybersecurity, focusing on its potential to enhance privacy-preserving detection of cyberattacks in cross-domain networks. As cyber threats evolve in complexity and scale, traditional centralized approaches face limitations in terms of data privacy and scalability. FL offers a decentralized framework where local models are trained on distributed data, ensuring privacy while enabling real-time threat detection. This chapter delves into the core concepts of FL, its relevance to cybersecurity, and its application in mitiga
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Prabakeran, S., T. Sethukarasi, and V. Indumathi. "Federated Learning Frameworks for Energy-Efficient AI in Distributed Data Centres." In Energy Efficient Algorithms and Green Data Centers for Sustainable Computing. IGI Global, 2025. https://doi.org/10.4018/979-8-3373-0766-4.ch016.

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The rising energy demands of large data centers call for energy-efficient AI training methods. Federated Learning (FL), a decentralized paradigm, offers a solution by enabling model training across distributed devices without centralizing sensitive data. This review explores FL's integration with distributed data centers to achieve energy efficiency, analyzing methods like federated averaging and energy-aware protocols to minimize resource use. It highlights techniques such as model compression, quantization, and adaptive FL to reduce on-device computation while maintaining performance. Practi
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Conference papers on the topic "Frameworks of FL"

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Li, Lei, and Hua Shen. "AFSA-FL: A flexible semi-asynchronous federated learning framework." In 2025 4th International Symposium on Computer Applications and Information Technology (ISCAIT). IEEE, 2025. https://doi.org/10.1109/iscait64916.2025.11010290.

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Arun Kumar Chavali, Satya Srinivasa, Akarsh K. Nair, and Jayakrushna Sahoo. "ComFLEX: A Communication efficient FL training framework for Edge computing environments." In 2025 International Wireless Communications and Mobile Computing (IWCMC). IEEE, 2025. https://doi.org/10.1109/iwcmc65282.2025.11059513.

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Kapoor, Ayshika, Sunil Datt Sharma, Dheeraj Kumar, and Sanjeev Narayan Sharma. "iProLSTM-FL: A Federated Learning Framework for Promoter Identification using LSTM Networks." In 2025 7th International Conference on Signal Processing, Computing and Control (ISPCC). IEEE, 2025. https://doi.org/10.1109/ispcc66872.2025.11039561.

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Patel, Pranshav, Naman Jain, Hrishita Patni, et al. "Explainable FL-Based Framework for Diagnosis of Diabetic Retinopathy in Healthcare 4.0 Environment." In 2024 10th International Conference on Signal Processing and Intelligent Systems (ICSPIS). IEEE, 2024. https://doi.org/10.1109/icspis65223.2024.10931114.

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Yao, Pengyu, Di Zhang, Min Guo, and Xun Shao. "HEGD-FL: A Privacy-Preserving Decentralized Federated Learning Framework Based on Homomorphic Encryption." In 2024 IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA). IEEE, 2024. https://doi.org/10.1109/ispa63168.2024.00012.

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Liu, Xiao, Lianming Xu, Xin Wu, Songyang Zhang, and Li Wang. "Split-FL: An Efficient Online Federated Learning Framework with Constrained Computation and Streaming Data." In 2024 IEEE International Conference on Communications Workshops (ICC Workshops). IEEE, 2024. http://dx.doi.org/10.1109/iccworkshops59551.2024.10615347.

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Ruan, Zeqi, Xiaolei Dong, Jiachen Shen, and Zhenfu Cao. "MC-DQE FL: Robust Federated Learning Framework Based on Multi-Criteria Data Quality Evaluation." In 2025 IEEE 6th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT). IEEE, 2025. https://doi.org/10.1109/ainit65432.2025.11035735.

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Li, Jialong, Jin Liu, Zhongdai Wu, and Junxiang Wang. "FL-CGF: An Edge-Federated Learning-Based Intelligent Intrusion Detection Framework for Heterogenous Maritime Internet of Things." In 2024 5th International Conference on Computer, Big Data and Artificial Intelligence (ICCBD+AI). IEEE, 2024. https://doi.org/10.1109/iccbd-ai65562.2024.00030.

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Woisetschläger, Herbert, Alexander Erben, Shiqiang Wang, Ruben Mayer, and Hans-Arno Jacobsen. "A Survey on Efficient Federated Learning Methods for Foundation Model Training." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/919.

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Federated Learning (FL) has become an established technique to facilitate privacy-preserving collaborative training across a multitude of clients. However, new approaches to FL often discuss their contributions involving small deep-learning models only and focus on training full models on clients. In the wake of Foundation Models (FM), the reality is different for many deep learning applications. Typically, FMs have already been pre-trained across a wide variety of tasks and can be fine-tuned to specific downstream tasks over significantly smaller datasets than required for full model training
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Zhang, Jiale, Chengcheng Zhu, Di Wu, Xiaobing Sun, Jianming Yong, and Guodong Long. "BADFSS: Backdoor Attacks on Federated Self-Supervised Learning." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/61.

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Self-supervised learning (SSL) is capable of learning remarkable representations from centrally available data. Recent works further implement federated learning with SSL to learn from rapidly growing decentralized unlabeled images (e.g., from cameras and phones), often resulting from privacy constraints. Extensive attention has been paid to designing new frameworks or methods that achieve better performance for the SSL-based FL. However, such an effort has not yet taken the security of SSL-based FL into consideration. We aim to explore backdoor attacks in the context of SSL-based FL via an in
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Reports on the topic "Frameworks of FL"

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Baader, Franz, and Ralf Molitor. Rewriting Concepts Using Terminologies. Aachen University of Technology, 1999. http://dx.doi.org/10.25368/2022.92.

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In this work we consider the inference problem of computing (minimal) rewritings of concept descriptions using defined concepts from a terminology. We introduce a general framework for this problem. For the small description logic FL₀, which provides us with conjunction and value restrictions, we show that the decision problem induced by the minimal rewriting problem is NP-complete.
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