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Journal articles on the topic 'Federated search'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

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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2

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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3

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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5

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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7

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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8

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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9

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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Bracke, Paul J., David K. Howse, and Samuel M. Keim. "Evidence-based Medicine Search: a customizable federated search engine." Journal of the Medical Library Association : JMLA 96, no. 2 (2008): 108–13. http://dx.doi.org/10.3163/1536-5050.96.2.108.

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12

Si, Luo. "Federated search of text search engines in uncooperative environments." ACM SIGIR Forum 41, no. 1 (2007): 120. http://dx.doi.org/10.1145/1273221.1273232.

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Deshmukh, Shamkant, Sonia Bhavsar, and Sandeep Bhavsar. "Open Source Software for Federated Search." DESIDOC Journal of Library & Information Technology 32, no. 5 (2012): 427–30. http://dx.doi.org/10.14429/djlit.32.5.2653.

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Belden, J., J. Williams, B. Richardson, K. Schuster, and D. Saparova. "Evaluating a federated medical search engine." Applied Clinical Informatics 05, no. 03 (2014): 731–45. http://dx.doi.org/10.4338/aci-2014-03-ra-0021.

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SummaryBackground: Federated medical search engines are health information systems that provide a single access point to different types of information. Their efficiency as clinical decision support tools has been demonstrated through numerous evaluations. Despite their rigor, very few of these studies report holistic evaluations of medical search engines and even fewer base their evaluations on existing evaluation frameworks.Objectives: To evaluate a federated medical search engine, MedSocket, for its potential net benefits in an established clinical setting.Methods: This study applied the Hu

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15

Lederman, Abe. "An architecture for scaling federated search." Proceedings of the American Society for Information Science and Technology 46, no. 1 (2009): 1–4. http://dx.doi.org/10.1002/meet.2009.1450460342.

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Saputra, Dwi Fajar, Zayyin Abdul Quddus, Putra Pertama Budianto, and Bintang Mahaputra Nararya Rabbani. "Federated Search in Libraries: An Overview in Academic Libraries." AL Maktabah 9, no. 1 (2024): 34. http://dx.doi.org/10.29300/mkt.v9i1.3559.

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One essential piece of technology that simplifies the process of finding information across a variety of databases and sources in academic libraries is federated search systems. By using a single query interface, these systems, also known as meta-search engines, allow users to simultaneously search through a variety of resources, such as databases, books, and electronic journals. By combining results from several publishers and platforms into a single format, this feature greatly improves the efficacy and efficiency of academic research while also saving time and streamlining user interactions

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Yang, An, and Ying Liu. "Heterogeneity-Aware Personalized Federated Neural Architecture Search." Entropy 27, no. 7 (2025): 759. https://doi.org/10.3390/e27070759.

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Federated learning (FL), which enables collaborative learning across distributed nodes, confronts a significant heterogeneity challenge, primarily including resource heterogeneity induced by different hardware platforms, and statistical heterogeneity originating from non-IID private data distributions among clients. Neural architecture search (NAS), particularly one-shot NAS, holds great promise for automatically designing optimal personalized models tailored to such heterogeneous scenarios. However, the coexistence of both resource and statistical heterogeneity destabilizes the training of th

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Joint, Nicholas. "Managing the implementation of a federated search tool in an academic library." Library Review 58, no. 1 (2009): 11–16. http://dx.doi.org/10.1108/00242530910928898.

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PurposeThe purpose of this paper is to look at some of the broader design and management issues that occur during the implementation of a federated search tool in a modern academic library, and tries to tease out some of the background concepts that librarians should consider.Design/methodology/approachAn overview of the main advantages and disadvantages of federated search tools in comparison with existing library tools and internet search engines such as Google.FindingsA successful implementation of a federated search engine was found to depend on a radical approach which subordinates other

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Caswell, Jerry V., and John D. Wynstra. "Improving the search experience: federated search and the library gateway." Library Hi Tech 28, no. 3 (2010): 391–401. http://dx.doi.org/10.1108/07378831011076648.

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20

Ceppi, Sofia, Nicola Gatti, and Enrico Gerding. "Mechanism Design for Federated Sponsored Search Auctions." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (2011): 608–13. http://dx.doi.org/10.1609/aaai.v25i1.7868.

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Recently there is an increase in smaller, domain-specific search engines that scour the deep web finding information that general-purpose engines are unable to discover. These search engines play a crucial role in the new generation of search paradigms where federated search engines (FSEs) integrate search results from heterogeneous sources. In this paper we pose, for the first time, the problem to design a revenue mechanism that ensures profits both to individual search engines and FSEs as a mechanism design problem. To this end, we extend the sponsored search auction models and we discuss po

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Mohamed, Khaled A., and Ahmed Hassan. "Evaluating Federated Search Tools : A Comparative Study." المجلة الدولية لعلوم المكتبات و المعلومات 1, no. 1 (2014): 145–75. http://dx.doi.org/10.12816/0010470.

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22

Fagan, Jody Condit. "Federated Search Is Dead—and Good Riddance!" Journal of Web Librarianship 5, no. 2 (2011): 77–79. http://dx.doi.org/10.1080/19322909.2011.573533.

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23

Yanofsky, Deena. "Students are Confident Using Federated Search Tools as much as Single Databases." Evidence Based Library and Information Practice 6, no. 3 (2011): 53. http://dx.doi.org/10.18438/b8sw5r.

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Objective – To measure students’ perceptions of the ease-of-use and efficacy of a federated search tool versus a single multidisciplinary database.   Design – An evaluation worksheet, employing a combination of quantitative and qualitative questions.  Setting – A required, first-year English composition course taught at the University of Illinois at Chicago (UIC).   Subjects – Thirty-one undergraduate students completed and submitted the worksheet.   Methods – Students attended two library instruction sessions. The first session introduced partic

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Belliston, C. Jeffrey, Jared L. Howland, and Brian C. Roberts. "Undergraduate Use of Federated Searching: A Survey of Preferences and Perceptions of Value-added Functionality." College & Research Libraries 68, no. 6 (2007): 472–87. http://dx.doi.org/10.5860/crl.68.6.472.

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Randomly selected undergraduates at Brigham Young University, Brigham Young University-Idaho, and Brigham Young University-Hawaii, all private universities sponsored by The Church of Jesus Christ of Latter-day Saints, participated in a study that investigated four questions regarding federated searching: (1) Does it save time? (2) Do undergraduates prefer it? (3) Are undergraduates satisfied with the results they get from it? (4) Does it yield higher-quality results than nonfederated searching? Federated searching was, on average, 11 percent faster than nonfederated searching. Undergraduates r

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25

Theobald, Matthew, Richard Sullivan, and Paul Thompson. "Internet Search Environment Number System: Search infrastructure for a federated environment." Procedia - Social and Behavioral Sciences 26 (2011): 159–65. http://dx.doi.org/10.1016/j.sbspro.2011.10.572.

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26

Koutsomitropoulos, Dimitrios, Georgia Solomou, and Katerina Kalou. "Federated semantic search using terminological thesauri for learning object discovery." Journal of Enterprise Information Management 30, no. 5 (2017): 795–808. http://dx.doi.org/10.1108/jeim-06-2016-0116.

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Purpose The purpose of this paper is to propose a framework and system to address the inability to discover new and authentic learning material and the lack of a single access point for search and browsing of remote learning object repositories (LORs). Design/methodology/approach The authors develop a framework for keyword-based query expansion using SKOS domain terminologies and implement a federated search mechanism integrating various disparate LORs within a learning management system (LMS). Findings The authors show that the expanded query achieves improved information gain and it is appli

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Tamizhchelvan, M., and S. Dhanavandan. "Federated Search Engines for Online Journals: A Study." Indian Journal of Information Sources and Services 3, no. 1 (2013): 48–52. http://dx.doi.org/10.51983/ijiss.2013.3.1.373.

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Search strategy in Information retrieval is important task to get proper results without wasting time. There were many developments in searching the articles or information from the internet. There are various search engines available to search e-journals, online databases. Federated search is new search technique to get one query with results from different online databases and online journals, etc. This paper made an attempt to identify the various tools available over the Internet.

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28

Veerabhadram, Vadlamani, and Gregory Arul Dalton. "Optimal and Efficient Searchable Encryption with Single Trapdoor for Multi-Owner Data Sharing in Federated Cloud Computing." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 6s (2023): 528–42. http://dx.doi.org/10.17762/ijritcc.v11i6s.6964.

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Cloud computing, an Internet based computing model, has changed the way of data owners store and manage data. In such environment, data sharing is very important with more efficient data access control. Issuing an aggregate key to users on data enables and authorizes them to search for data of select encrypted files using trapdoor or encrypted keyword. The existing schemes defined for this purpose do have certain limitations. For instance, Cui et al. scheme is elegant but lacks in flexibility in access control in presence of multiple data owners sharing data to users. Its single trapdoor appro

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29

Prieto, Joel Manuel. "Psychological state of runners. Differences between federated and non-federated." Revista Guillermo de Ockham 16, no. 1 (2018): 45–51. http://dx.doi.org/10.21500/22563202.3845.

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The purpose of this study is to meet the motivations and psychological state of the popular runners, differentiating between federated and non-federated, and between men and women. The sample was composed of 473 popular runners, using Motivations of Marathoners Scales (MOMS) and CSAI-2R to evaluate motivation and anxiety. The results indicate that federated runners present higher scores in sense of life and lower scores in self-confidence, achievement of personal goal, self-esteem and recognition search. On the other hand, the men obtained significantly higher scores on physical fitness, socia

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Gore, Genevieve. "Undergraduates Prefer Federated Searching to Searching Databases Individually." Evidence Based Library and Information Practice 3, no. 3 (2008): 61. http://dx.doi.org/10.18438/b8b318.

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A Review of: Belliston, C. Jeffrey, Jared L. Howland, & Brian C. Roberts. “Undergraduate Use of Federated Searching: A Survey of Preferences and Perceptions of Value-Added Functionality.” College & Research Libraries 68.6 (Nov. 2007): 472-86.   Objective – To determine whether use of federated searching by undergraduates saves time, meets their information needs, is preferred over searching databases individually, and provides results of higher quality.  Design – Crossover study.  Setting – Three American universities, all members of the

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31

Fillmann Barcelos, Carla, João Carlos Gluz, and Rosa Maria Vicari. "An Agent-based Federated Learning Object Search Service." Interdisciplinary Journal of e-Skills and Lifelong Learning 7 (2011): 037–54. http://dx.doi.org/10.28945/1355.

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32

Lederman, Abe, Walter Warnick, Brian Hitson, and Lorrie Johnson. "Breaking down language barriers through multilingual federated search." Information Services & Use 30, no. 3-4 (2011): 125–32. http://dx.doi.org/10.3233/isu-2010-0617.

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33

George, Carole A. "Lessons learned: usability testing a federated search product." Electronic Library 26, no. 1 (2008): 5–20. http://dx.doi.org/10.1108/02640470810851707.

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34

Mohamed, Khaled A., and Ahmed Hassan. "Evaluating federated search tools: usability and retrievability framework." Electronic Library 33, no. 6 (2015): 1079–99. http://dx.doi.org/10.1108/el-12-2013-0211.

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35

Zhu, Yongqing, Quanqing Xu, Haixiang Shi, and Juniarto Samsudin. "An efficient distributed search solution for federated cloud." Distributed and Parallel Databases 35, no. 3-4 (2017): 411–33. http://dx.doi.org/10.1007/s10619-017-7201-5.

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36

Salampasis, Michail, and Allan Hanbury. "PerFedPat: An integrated federated system for patent search." World Patent Information 38 (September 2014): 4–11. http://dx.doi.org/10.1016/j.wpi.2014.08.001.

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37

Taylor, Mary. "Using the Google Search Appliance for Federated Searching." Internet Reference Services Quarterly 10, no. 3-4 (2005): 45–55. http://dx.doi.org/10.1300/j136v10n03_06.

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38

Fang, Hui, Yang Gao, Peng Zhang, Jiangchao Yao, Hongyang Chen, and Haishuai Wang. "Large Language Models Enhanced Personalized Graph Neural Architecture Search in Federated Learning." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 16 (2025): 16514–22. https://doi.org/10.1609/aaai.v39i16.33814.

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Personalized federated learning (PFL) on graphs is an emerging field focusing on the collaborative development of architectures across multiple clients, each with distinct graph data distributions while adhering to strict privacy standards. This area often requires extensive expert intervention in model design, which is a significant limitation. Recent advancements have aimed to automate the search for graph neural network architectures, incorporating large language models (LLMs) for their advanced reasoning and self-reflection capabilities. However, two technical challenges persist. First, al

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39

Martin, Rob. "Integrated Search Strategies for the Modern Law Firm." Legal Information Management 7, no. 4 (2007): 263–68. http://dx.doi.org/10.1017/s1472669607002083.

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AbstractThis article by Rob Martin addresses the issue of effectively managing the searching of enterprise information systems in law firms. It discusses the relative merits of federated, enterprise and integrated search and includes a case study of the combined solution adopted by City law firm, Ashurst.

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40

Ghansah, Benjamin, and Shengli Wu. "A Mean-Variance Analysis Based Approach for Search Result Diversification in Federated Search." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 24, no. 02 (2016): 195–211. http://dx.doi.org/10.1142/s0218488516500100.

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Resource Selection is an important step in a federated search environment. The goal of this work was to improve the collection selection process by selecting collections in terms of relevance and diversity, to best answer a user's query. Sampled documents from the Central Sample Database are first ranked by Indri retrieval algorithm and later re-ranked by a Mean-Standard deviation method that reduces uncertainty and improves diversity of collection sources. A comparative evaluation with the R-based diversification metrics shows that the proposed method significantly outperforms the baseline di

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41

Yang, Zhao, and Qingshuang Sun. "Energy-efficient Personalized Federated Search with Graph for Edge Computing." ACM Transactions on Embedded Computing Systems 22, no. 5s (2023): 1–24. http://dx.doi.org/10.1145/3609435.

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Federated Learning (FL) is a popular method for privacy-preserving machine learning on edge devices. However, the heterogeneity of edge devices, including differences in system architecture, data, and co-running applications, can significantly impact the energy efficiency of FL. To address these issues, we propose an energy-efficient personalized federated search framework. This framework has three key components. Firstly, we search for partial models with high inference efficiency to reduce training energy consumption and the occurrence of stragglers in each round. Secondly, we build lightwei

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42

Garba, Adamu, Shah Khalid, Irfan Ullah, Shah Khusro, and Diyawu Mumin. "Embedding based learning for collection selection in federated search." Data Technologies and Applications 54, no. 5 (2020): 703–17. http://dx.doi.org/10.1108/dta-01-2019-0005.

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PurposeThere have been many challenges in crawling deep web by search engines due to their proprietary nature or dynamic content. Distributed Information Retrieval (DIR) tries to solve these problems by providing a unified searchable interface to these databases. Since a DIR must search across many databases, selecting a specific database to search against the user query is challenging. The challenge can be solved if the past queries of the users are considered in selecting collections to search in combination with word embedding techniques. Combining these would aid the best performing collec

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43

Lu, Jie. "Full-text federated search in peer-to-peer networks." ACM SIGIR Forum 41, no. 1 (2007): 121. http://dx.doi.org/10.1145/1273221.1273233.

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Joint, Nicholas. "Federated search engines and the development of library systems." Library Review 57, no. 9 (2008): 653–59. http://dx.doi.org/10.1108/00242530810911770.

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45

Xu, Fei. "Implementation of a Federated Search System: Resource Accessibility Issues." Serials Review 35, no. 4 (2009): 235–41. http://dx.doi.org/10.1080/00987913.2009.10765251.

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46

Bogstad, Janice M. "Federated Search: Solution or Setback for Online Library Services." Library Collections, Acquisitions, and Technical Services 34, no. 1 (2010): 43. http://dx.doi.org/10.1016/j.lcats.2009.08.002.

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47

Baillie, Mark, Mark Carman, and Fabio Crestani. "A multi-collection latent topic model for federated search." Information Retrieval 14, no. 4 (2010): 390–412. http://dx.doi.org/10.1007/s10791-010-9147-3.

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48

Lampert, Lynn D., and Katherine S. Dabbour. "Librarian Perspectives on Teaching Metasearch and Federated Search Technologies." Internet Reference Services Quarterly 12, no. 3-4 (2007): 253–78. http://dx.doi.org/10.1300/j136v12n03_02.

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Bogstad, Janice M. "Federated Search: Solution or Setback for Online Library Services." Library Collections, Acquisitions, & Technical Services 34, no. 1 (2010): 43. http://dx.doi.org/10.1080/14649055.2010.10766257.

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50

Avrahami, Thi Truong, Lawrence Yau, Luo Si, and Jamie Callan. "The FedLemur project: Federated search in the real world." Journal of the American Society for Information Science and Technology 57, no. 3 (2006): 347–58. http://dx.doi.org/10.1002/asi.20283.

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