Relevant bibliographies by topics / Federated search

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Academic literature on the topic 'Federated search'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Federated search"

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Shokouhi, Milad, and Luo Si. "Federated Search." Foundations and Trends® in Information Retrieval 5, no. 1 (2011): 1–102. http://dx.doi.org/10.1561/1500000010.

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Boss, Stephen C., and Michael L. Nelson. "Federated Search Tools." Reference Librarian 44, no. 91-92 (2005): 139–60. http://dx.doi.org/10.1300/j120v44n91_10.

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Zhang, Xinyi, Qichen Wang, Cheng Xu, Yun Peng, and Jianliang Xu. "FedKNN: Secure Federated k-Nearest Neighbor Search." Proceedings of the ACM on Management of Data 2, no. 1 (2024): 1–26. http://dx.doi.org/10.1145/3639266.

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Nearest neighbor search is a fundamental task in various domains, such as federated learning, data mining, information retrieval, and biomedicine. With the increasing need to utilize data from different organizations while respecting privacy regulations, private data federation has emerged as a promising solution. However, it is costly to directly apply existing approaches to federated k-nearest neighbor (kNN) search with difficult-to-compute distance functions, like graph or sequence similarity. To address this challenge, we propose FedKNN, a system that supports secure federated kNN search q
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S Lingam, Arunadevi. "Federated search and discovery solutions." IP Indian Journal of Library Science and Information Technology 5, no. 1 (2020): 39–42. http://dx.doi.org/10.18231/j.ijlsit.2020.008.

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Arguello, Jaime. "Federated search in heterogeneous environments." ACM SIGIR Forum 46, no. 1 (2012): 78–79. http://dx.doi.org/10.1145/2215676.2215686.

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Rainwater, Jean. "Maintaining a Federated Search Service." Internet Reference Services Quarterly 12, no. 3-4 (2007): 309–23. http://dx.doi.org/10.1300/j136v12n03_05.

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Schatz, B., W. Mischo, T. Cole, et al. "Federated search of scientific literature." Computer 32, no. 2 (1999): 51–59. http://dx.doi.org/10.1109/2.745720.

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Yao, Dixi, and Baochun Li. "PerFedRLNAS: One-for-All Personalized Federated Neural Architecture Search." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 15 (2024): 16398–406. http://dx.doi.org/10.1609/aaai.v38i15.29576.

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Personalized federated learning is a new paradigm to address heterogeneous problems (e.g. issues with non-i.i.d. data) in federated learning. However, existing personalized federated learning methods lack standards for how personalized and shared parts of the models are designed. Sometimes, manual design can even lead to worse performance than non-personalization. As a result, we propose a new algorithm for personalized federated neural architecture search, called PerFedRLNAS, to automatically personalize the architectures and weights of models on each client. With such an algorithm, we can so
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Woods, Roberta F. "From Federated Search to the Universal Search Solution." Serials Librarian 58, no. 1-4 (2010): 141–48. http://dx.doi.org/10.1080/03615261003622957.

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Jiang, Hong, Tianxu Cui, and Kaiwen Yang. "Design of Sponsored Search Auction Mechanism for Federated Learning Advertising Platform." Computational Intelligence and Neuroscience 2022 (April 7, 2022): 1–15. http://dx.doi.org/10.1155/2022/5787491.

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Federated learning has demonstrated strong capabilities in terms of addressing concerns related to data islands and privacy protection. However, in real application scenarios, participants in federated learning have difficulty matching. For example, two companies distributed in different regions do not know that the other party also needs federated learning in the case of information asymmetry. Therefore, it is difficult to build alliances. To enable suppliers and consumers to find one or more federated learning objects that are relatively satisfactory in a short time, this paper considers the
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Dissertations / Theses on the topic "Federated search"

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Schatz, Bruce R., William Mischo, Timothy Cole, et al. "Federated Search of Scientific Literature." IEEE, 1999. http://hdl.handle.net/10150/105326.

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Artificial Intelligence Lab, Department of MIS, University of Arizona<br>The Digital Libraries Initiative (DLI) project at the University of Illinois at Urbana-Champaign (UIUC) was one of six sponsored by the NSF, DARPA, and NASA from 1994 through 1998. Our goal was to develop widely usable Web technology to effectively search technical documents on the Internet. We concentrated on building the experimental Illinois DLI Testbed with tens of thousands of full-text journal articles from physics, engineering, and computer science, and on making these articles available over the Internet before t
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Shokouhi, Milad, and milads@microsoft com. "Federated Text Retrieval from Independent Collections." RMIT University. Computer Science and Information Technology, 2008. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20080521.151632.

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Federated information retrieval is a technique for searching multiple text collections simultaneously. Queries are submitted to a subset of collections that are most likely to return relevant answers. The results returned by selected collections are integrated and merged into a single list. Federated search is preferred over centralized search alternatives in many environments. For example, commercial search engines such as Google cannot index uncrawlable hidden web collections; federated information retrieval systems can search the contents of hidden web collections without crawling. In enter
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IZQUIERDO, YENIER TORRES. "KEYWORD SEARCH OVER FEDERATED RDF GRAPHS BY EXPLORING THEIR SCHEMAS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2017. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=30739@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO<br>O Resource Description Framework (RDF) foi adotado como uma recomendação do W3C em 1999 e hoje é um padrão para troca de dados na Web. De fato, uma grande quantidade de dados foi convertida em RDF, muitas vezes em vários conjuntos de dados fisicamente distribuídos ao longo de diferentes localizações. A linguagem de consulta SPARQL (sigla do inglês de SPARQL Protocol and RDF Query Language) foi oficialmente introduzido em 2008 para recuperar dados RDF e fornecer endpoints para
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Walther, Maximilian Thilo. "Federated Product Information Search and Semantic Product Comparisons on the Web." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2011. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-74759.

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Product information search has become one of the most important application areas of the Web. Especially considering pricey technical products, consumers tend to carry out intensive research activities previous to the actual acquisition for creating an all-embracing view on the product of interest. Federated search backed by ontology-based product information representation shows great promise for easing this research process. The topic of this thesis is to develop a comprehensive technique for locating, extracting, and integrating information of arbitrary technical products in a widely unsupe
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Wormet, Jody R. "Federated Search Tools in Fusion Centers : Bridging Databases in the Information Sharing Environment." Thesis, Monterey, California. Naval Postgraduate School, 2012. http://hdl.handle.net/10945/17480.

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Approved for public release; distribution is unlimited<br>This research utilized a semi-structured survey instrument delivered to subject matter experts within the national network of fusion centers and employed a constant comparison method to analyze the survey results. This smart practice exploration informed through an appreciative inquiry lens found considerable variation in how fusion centers plan for, gather requirements, select and acquire federated search tools to bridge disparate databases. These findings confirmed the initial hypothesis that fusion centers have received very little g
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Schatz, Bruce R., William Mischo, Timothy Cole, et al. "Federated Search of Scientific Literatures: A Retrospective on the Illinios Digital Library Project." UIUC, 2000. http://hdl.handle.net/10150/106134.

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Artificial Intelligence Lab, Department of MIS, University of Arizona<br>The NSF/DARPA/NASA Digital Libraries Initiative (DLI) project at the University of Illinois at Urbana-Champaign (UIUC), 1994-1998, had the goal of developing widely usable Web technology to effectively search technical documents on the Internet. The DLI testbed focused on using the document structure to provide federated searches across publisher collections. Our sociology research included the evaluation of its effectiveness under use by over 1,000 UIUC faculty and students, a user community an order of magnitude bigger
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Walther, Maximilian Thilo [Verfasser], Alexander [Akademischer Betreuer] Schill, and Claudia [Akademischer Betreuer] Linnhoff-Popien. "Federated Product Information Search and Semantic Product Comparisons on the Web / Maximilian Thilo Walther. Gutachter: Alexander Schill ; Claudia Linnhoff-Popien. Betreuer: Alexander Schill." Dresden : Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2011. http://d-nb.info/1067190724/34.

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Mödritscher, Felix, Barbara Krumay, Helou Sandy El, et al. "May I Suggest? Comparing Three PLE Recommender Strategies." Universitat de Barcelona, 2011. http://epub.wu.ac.at/6078/1/Krumay_etal_2011_DER_May%2DI%2DSuggest.pdf.

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Personal learning environment (PLE) solutions aim at empowering learners to design (ICT and web-based) environments for their learning activities, mashingup content and people and apps for different learning contexts. Widely used in other application areas, recommender systems can be very useful for supporting learners in their PLE-based activities, to help discover relevant content, peers sharing similar learning interests or experts on a specific topic. In this paper we examine the utilization of recommender technology for PLEs. However, being confronted by a variety of educational contexts
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Kim, Jungkee Riccardi Greg. "Hybrid keyword search across peer-to-peer federated data." Diss., 2005. http://etd.lib.fsu.edu/theses/available/etd-04142005-183110.

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Thesis (Ph. D.)--Florida State University, 2005.<br>Advisor: Dr. Gregory Riccardi, Florida State University, College of Arts and Sciences, Dept. of Computer Science. Title and description from dissertation home page (viewed June 7, 2005). Document formatted into pages; contains ix, 102 pages. Includes bibliographical references.
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Lin, Mao-Hsiung, and 林茂雄. "A Recommender System Based on Personal Ontology Model for Federated Search." Thesis, 2019. http://ndltd.ncl.edu.tw/cgi-bin/gs32/gsweb.cgi/login?o=dnclcdr&s=id=%22107NCHU5394027%22.&searchmode=basic.

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碩士<br>國立中興大學<br>資訊科學與工程學系所<br>107<br>In order to get current research trends and topics relating to the fields of study, scholars and researchers often need to collect a large amount of papers and articles. One of the most common ways is to get the information by querying online specialized databases (such as IEEE Xplore Digital Library, ACM, etc...). However, it is often necessary to spend a considerable amount of time for searching data from multiple independent online databases. Therefore, the "Federated search system" is proposed. When the user enters a keyword, the system will return a li
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Books on the topic "Federated search"

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N, Cox Christopher, ed. Federated search: Solution or setback for online library services. Haworth Information Press, 2007.

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Cox, Christopher N., ed. Federated Search. Routledge, 2013. http://dx.doi.org/10.4324/9781315871776.

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Shokouhi, Milad, and Luo Si. Federated Search. Now Publishers, 2011.

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Federated Search: Solution or Setback for Online Library Services. The Haworth Press Inc, 2006.

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Cox, Christopher N. Federated Search: Solution or Setback for Online Library Services. Taylor & Francis Group, 2013.

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Book chapters on the topic "Federated search"

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Salampasis, Michail. "Federated Patent Search." In Current Challenges in Patent Information Retrieval. Springer Berlin Heidelberg, 2017. http://dx.doi.org/10.1007/978-3-662-53817-3_8.

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Liu, Yang, Xinle Liang, Jiahuan Luo, et al. "Cross-Silo Federated Neural Architecture Search for Heterogeneous and Cooperative Systems." In Federated and Transfer Learning. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11748-0_4.

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Wang, Qing, Peng Peng, Tianyao Tong, Zhen Tian, and Zheng Qin. "Keyword Search over Federated RDF Systems." In Database Systems for Advanced Applications. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59416-9_37.

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Mizera-Pietraszko, Jolanta, and Aleksander Zgrzywa. "Vertical Search Strategy in Federated Environment." In Advances in Intelligent and Soft Computing. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14989-4_21.

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Thomas, Paul, and Milad Shokouhi. "Evaluating Server Selection for Federated Search." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12275-0_58.

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Damas, João, José Devezas, and Sérgio Nunes. "Federated Search Using Query Log Evidence." In Progress in Artificial Intelligence. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16474-3_64.

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Pedro, Vasco Calais, Lucian Vlad Lita, Stefan Niculescu, Bharat Rao, and Jaime Carbonell. "Federated Ontology Search for the Medical Domain." In On the Move to Meaningful Internet Systems 2007: OTM 2007 Workshops. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-76888-3_78.

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Wauer, Matthias, Daniel Schuster, and Alexander Schill. "Advanced Resource Selection for Federated Enterprise Search." In Business Information Systems Workshops. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25370-6_15.

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Zhu, Hangyu, and Yaochu Jin. "Toward Real-Time Federated Evolutionary Neural Architecture Search." In Automated Design of Machine Learning and Search Algorithms. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72069-8_8.

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Herzig, Daniel M., Peter Mika, Roi Blanco, and Thanh Tran. "Federated Entity Search Using On-the-Fly Consolidation." In Advanced Information Systems Engineering. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41335-3_11.

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Conference papers on the topic "Federated search"

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Ramírez-Gordillo, Tamai, Higinio Mora, Francisco A. Pujol, and Antonio Maciá-Lillo. "FedNAS: A Distributed Neural Architecture Search." In 2024 2nd International Conference on Federated Learning Technologies and Applications (FLTA). IEEE, 2024. https://doi.org/10.1109/flta63145.2024.10839887.

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Wei, Xiaohui, Guanhua Chen, Chen Yang, Hairui Zhao, Chenyang Wang, and Hengshan Yue. "EFNAS: Efficient Federated Neural Architecture Search Across AIoT Devices." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10650653.

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Yao, Dixi. "Revisiting System-Heterogeneous Federated Learning through Dynamic Model Search." In 2024 IEEE International Conference on Big Data (BigData). IEEE, 2024. https://doi.org/10.1109/bigdata62323.2024.10825396.

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Huo, Chao, Juncheng Jia, Tao Deng, Mianxiong Dong, Zhanwei Yu, and Di Yuan. "NASFLY: On-Device Split Federated Learning with Neural Architecture Search." In 2024 IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA). IEEE, 2024. https://doi.org/10.1109/ispa63168.2024.00298.

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Ramírez-Gordillo, Tamai, Higinio Mora, Francisco A. Pujol, and Antonio Maciá-Lillo. "Federated Neural Architecture Search for Efficient and Privacy-Preserving Model Training." In 2024 IEEE International Conference on Smart Internet of Things (SmartIoT). IEEE, 2024. https://doi.org/10.1109/smartiot62235.2024.00028.

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Mi, Bo, Ran Zeng, Ling Zhao, Siyuan Zeng, Fuyuan Wang, and Qi Zhou. "Conjugacy Search Problem Homomorphic Encryption Based Vertically Federated Graph Neural Network." In 2024 IEEE 7th International Conference on Industrial Cyber-Physical Systems (ICPS). IEEE, 2024. http://dx.doi.org/10.1109/icps59941.2024.10640045.

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Shah, Milavkumar, Parth Shah, and Swapnil Patil. "Secure and Efficient Fraud Detection Using Federated Learning and Distributed Search Databases." In 2025 IEEE 4th International Conference on AI in Cybersecurity (ICAIC). IEEE, 2025. https://doi.org/10.1109/icaic63015.2025.10849280.

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Amato, Flora, Egidia Cirillo, Mattia Fonisto, and Alberto Moccardi. "Optimizing Legal Information Access: Federated Search and RAG for Secure AI-Powered Legal Solutions." In 2024 IEEE International Conference on Big Data (BigData). IEEE, 2024. https://doi.org/10.1109/bigdata62323.2024.10825815.

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Chang, Qi, Lin Fan, You Wu, and Yihui He. "Cooperation among Multiple Medical Institutions on Breast Cancer Survival Status Prediction Based on Federated Learning and Monte Carlo Trees Search." In 2025 5th International Conference on Neural Networks, Information and Communication Engineering (NNICE). IEEE, 2025. https://doi.org/10.1109/nnice64954.2025.11063917.

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Si, Luo, and Jamie Callan. "Modeling search engine effectiveness for federated search." In the 28th annual international ACM SIGIR conference. ACM Press, 2005. http://dx.doi.org/10.1145/1076034.1076051.

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