{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T18:33:50Z","timestamp":1780338830344,"version":"3.54.1"},"publisher-location":"Cham","reference-count":8,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030923068","type":"print"},{"value":"9783030923075","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-92307-5_56","type":"book-chapter","created":{"date-parts":[[2021,12,6]],"date-time":"2021-12-06T14:04:20Z","timestamp":1638799460000},"page":"480-487","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Fed-FiS: a\u00a0Novel Information-Theoretic Federated Feature Selection for\u00a0Learning Stability"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3451-2851","authenticated-orcid":false,"given":"Sourasekhar","family":"Banerjee","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2633-6798","authenticated-orcid":false,"given":"Erik","family":"Elmroth","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9842-7840","authenticated-orcid":false,"given":"Monowar","family":"Bhuyan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,12,2]]},"reference":[{"key":"56_CR1","series-title":"EAI\/Springer Innovations in Communication and Computing","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1007\/978-3-030-35280-6_9","volume-title":"Applications in Ubiquitous Computing","author":"G Manikandan","year":"2021","unstructured":"Manikandan, G., Abirami, S.: Feature selection is important: state-of-the-art methods and application domains of feature selection on high-dimensional data. In: Kumar, R., Paiva, S. (eds.) Applications in Ubiquitous Computing. EICC, pp. 177\u2013196. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-35280-6_9"},{"issue":"14","key":"56_CR2","doi-asserted-by":"publisher","first-page":"6371","DOI":"10.1016\/j.eswa.2014.04.019","volume":"41","author":"N Hoque","year":"2014","unstructured":"Hoque, N., et al.: MIFS-ND: a mutual information-based feature selection method. Expert Syst. Appl. 41(14), 6371\u20136385 (2014)","journal-title":"Expert Syst. Appl."},{"key":"56_CR3","doi-asserted-by":"publisher","first-page":"2150021","DOI":"10.1142\/S021800142150021X","volume":"35","author":"G Liu","year":"2021","unstructured":"Liu, G., et al.: Feature selection method based on mutual information and support vector machine. Int. J. Pattern Recogn. Artif. Intell. 35, 2150021 (2021)","journal-title":"Int. J. Pattern Recogn. Artif. Intell."},{"key":"56_CR4","doi-asserted-by":"publisher","first-page":"1256","DOI":"10.1016\/j.ins.2020.09.022","volume":"546","author":"L Zheng","year":"2021","unstructured":"Zheng, L., et al.: Feature grouping and selection: a graph-based approach. Inf. Sci. 546, 1256\u20131272 (2021)","journal-title":"Inf. Sci."},{"key":"56_CR5","doi-asserted-by":"crossref","unstructured":"Gui, Y.: ADAGES: adaptive aggregation with stability for distributed feature selection. In: Proceedings of the ACM-IMS on Foundations of Data Science Conference, pp. 3\u201312 (2020)","DOI":"10.1145\/3412815.3416881"},{"key":"56_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jpdc.2019.12.001","volume":"138","author":"M Soheili","year":"2020","unstructured":"Soheili, M., et al.: DQPFS: distributed quadratic programming based feature selection for big data. J. Parallel Distrib. Comput. 138, 1\u201314 (2020)","journal-title":"J. Parallel Distrib. Comput."},{"key":"56_CR7","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.knosys.2016.09.022","volume":"117","author":"L Mor\u00e1n-Fern\u00e1ndez","year":"2017","unstructured":"Mor\u00e1n-Fern\u00e1ndez, L., et al.: Centralized vs. distributed feature selection methods based on data complexity measures. Knowl.-Based Syst. 117, 27\u201345 (2017)","journal-title":"Knowl.-Based Syst."},{"key":"56_CR8","doi-asserted-by":"crossref","unstructured":"Tavallaee, M., et al.: A detailed analysis of the KDD CUP 99 data set. In: IEEE Symposium on Computational Intelligence for Security and Defense Applications, pp. 1\u20136 (2009)","DOI":"10.1109\/CISDA.2009.5356528"}],"container-title":["Communications in Computer and Information Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-92307-5_56","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,6]],"date-time":"2021-12-06T14:35:44Z","timestamp":1638801344000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-92307-5_56"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030923068","9783030923075"],"references-count":8,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-92307-5_56","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"2 December 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sanur, Bali","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Indonesia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 December 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 December 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iconip2021.apnns.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1093","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"226","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"177","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"21% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.57","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"6","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Due to the COVID-19 pandemic the conference was held online.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}