{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T09:23:59Z","timestamp":1743067439511,"version":"3.40.3"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031578076"},{"type":"electronic","value":"9783031578083"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-57808-3_31","type":"book-chapter","created":{"date-parts":[[2024,4,5]],"date-time":"2024-04-05T15:02:05Z","timestamp":1712329325000},"page":"425-437","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["FedPV-FS: A Feature Selection Method for Federated Learning in Insurance Precision Marketing"],"prefix":"10.1007","author":[{"given":"Chunkai","family":"Wang","sequence":"first","affiliation":[]},{"given":"Jian","family":"Feng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,4,6]]},"reference":[{"key":"31_CR1","unstructured":"McMahan, H.B., Moore, E., Ramage, D., et al.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273\u20131282. PMLR (2017)"},{"issue":"3","key":"31_CR2","first-page":"1","volume":"13","author":"Q Yang","year":"2019","unstructured":"Yang, Q., Liu, Y., Cheng, Y., et al.: Federated learning. Synth. Lect. Artif. Intell. Mach. Learn. 13(3), 1\u2013207 (2019)","journal-title":"Synth. Lect. Artif. Intell. Mach. Learn."},{"key":"31_CR3","doi-asserted-by":"crossref","unstructured":"Wang, J., Zhang, A., Li, X., et al.: Efficient participant contribution evaluation for horizontal and vertical federated learning. In: 2022 IEEE 38th International Conference on Data Engineering (ICDE), pp. 911\u2013923 (2022)","DOI":"10.1109\/ICDE53745.2022.00073"},{"issue":"1","key":"31_CR4","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.compeleceng.2013.11.024","volume":"40","author":"G Chandrashekar","year":"2014","unstructured":"Chandrashekar, G., Sahin, F.: A survey on feature selection methods. Comput. Electr. Eng. 40(1), 16\u201328 (2014)","journal-title":"Comput. Electr. Eng."},{"key":"31_CR5","unstructured":"Pan, F., Meng, D., Zhang, Y., et al.: Secure federated feature selection for cross-feature federated learning (2020)"},{"key":"31_CR6","doi-asserted-by":"crossref","unstructured":"Yao, A.: Protocols for secure computations. In: 23rd Annual Symposium on Foundations of Computer Science, pp. 160\u2013164. IEEE Computer Society, Chicago, Illinois, USA (1982)","DOI":"10.1109\/SFCS.1982.38"},{"key":"31_CR7","doi-asserted-by":"crossref","unstructured":"Yang, Z., Sun, Q.: Joint think locally and globally: communication-efficient federated learning with feature-aligned filter selection. Comput. Commun. (2023)","DOI":"10.1016\/j.comcom.2023.03.002"},{"key":"31_CR8","doi-asserted-by":"crossref","unstructured":"Mahanipour, A., Khamfroush, H.: Wrapper-based federated feature selection for iot environments. In: 2023 International Conference on Computing, Networking and Communications (ICNC), Honolulu, HI, USA, pp. 214\u2013219 (2023)","DOI":"10.1109\/ICNC57223.2023.10074296"},{"key":"31_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.sysarc.2022.102474","volume":"126","author":"P Chen","year":"2022","unstructured":"Chen, P., Du, X., Lu, Z., et al.: EVFL: an explainable vertical federated learning for data-oriented artificial intelligence systems. J. Syst. Archit. 126, 102474 (2022)","journal-title":"J. Syst. Archit."},{"key":"31_CR10","doi-asserted-by":"crossref","unstructured":"Feng, S.: Vertical federated learning-based feature selection with non-overlapping sample utilization. Expert Syst. Appl. (2022)","DOI":"10.1016\/j.eswa.2022.118097"},{"key":"31_CR11","doi-asserted-by":"crossref","unstructured":"Li, A., Peng, H., Zhang, L., et al.: edSDG-FS: efficient and secure feature selection for vertical federated learning. In: IEEE International Conference on Computer Communication (2023)","DOI":"10.1109\/INFOCOM53939.2023.10228895"},{"key":"31_CR12","unstructured":"Louizos, C., Welling, M., Kingma, D.: Learning sparse neural networks through l0 regularization. arXiv:1712.01312 (2018)"},{"key":"31_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1007\/978-3-030-17659-4_4","volume-title":"Advances in Cryptology \u2013 EUROCRYPT 2019","author":"C Hong","year":"2019","unstructured":"Hong, C., Katz, J., Kolesnikov, V., Lu, W., Wang, X.: Covert security with public verifiability: faster, leaner, and simpler. In: Ishai, Y., Rijmen, V. (eds.) EUROCRYPT 2019. LNCS, vol. 11478, pp. 97\u2013121. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-17659-4_4"},{"key":"31_CR14","doi-asserted-by":"crossref","unstructured":"Feldman, P.: A practical scheme for non-interactive verifiable secret sharing. In: 28th Annual Symposium on Foundations of Computer Science, Los Angeles, CA, USA, pp. 427\u2013438 (1987)","DOI":"10.1109\/SFCS.1987.4"},{"key":"31_CR15","doi-asserted-by":"crossref","unstructured":"Even, S., Goldreich, O., Lempel, A.: A randomized protocol for signing contracts. Commun. ACM, 637\u2013647 (1985)","DOI":"10.1145\/3812.3818"},{"key":"31_CR16","unstructured":"FATE Homepage. https:\/\/github.com\/FederatedAI\/FATE. Accessed 30 Nov 2023"},{"key":"31_CR17","unstructured":"J. Thomas. Mass spectrometric data. https:\/\/www.openml.org\/d\/41157"},{"key":"31_CR18","unstructured":"Dua, D., Graff, C.: UCI machine learning repository (2017)"},{"key":"31_CR19","unstructured":"Cheng, K., Fan, T., Jin, Y., et al.:SecureBoost: a lossless federated learning framework. arXiv (2019)"},{"key":"31_CR20","doi-asserted-by":"crossref","unstructured":"Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. CoRR abs\/1603.02754 (2016)","DOI":"10.1145\/2939672.2939785"}],"container-title":["IFIP Advances in Information and Communication Technology","Intelligent Information Processing XII"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-57808-3_31","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,5]],"date-time":"2024-04-05T15:06:25Z","timestamp":1712329585000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-57808-3_31"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031578076","9783031578083"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-57808-3_31","relation":{},"ISSN":["1868-4238","1868-422X"],"issn-type":[{"type":"print","value":"1868-4238"},{"type":"electronic","value":"1868-422X"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"6 April 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shenzhen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 May 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 May 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iip2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/mi.hitsz.edu.cn\/iip2024.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair online submission","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"58","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":"49","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":"5","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":"84% - 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":"4","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":"4","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"7 Abstracts include 4 Keynotes speakers and 3 Invited Speakers","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)"}}]}}