{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T13:53:07Z","timestamp":1762005187883,"version":"3.40.3"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030375980"},{"type":"electronic","value":"9783030375997"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-37599-7_58","type":"book-chapter","created":{"date-parts":[[2020,1,3]],"date-time":"2020-01-03T09:02:43Z","timestamp":1578042163000},"page":"700-710","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Federated Learning of Deep Neural Decision Forests"],"prefix":"10.1007","author":[{"given":"Anders","family":"Sj\u00f6berg","sequence":"first","affiliation":[]},{"given":"Emil","family":"Gustavsson","sequence":"additional","affiliation":[]},{"given":"Ashok Chaitanya","family":"Koppisetty","sequence":"additional","affiliation":[]},{"given":"Mats","family":"Jirstrand","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,1,3]]},"reference":[{"key":"58_CR1","unstructured":"Bojarski, M., et al.: End to end learning for self-driving cars. arXiv preprint \narXiv:1604.07316\n\n (2016)"},{"issue":"1","key":"58_CR2","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45(1), 5\u201332 (2001)","journal-title":"Mach. Learn."},{"key":"58_CR3","doi-asserted-by":"crossref","unstructured":"Caruana, R., Karampatziakis, N., Yessenalina, A.: An empirical evaluation of supervised learning in high dimensions. In: Proceedings of the 25th International Conference on Machine Learning, pp. 96\u2013103. ACM (2008)","DOI":"10.1145\/1390156.1390169"},{"key":"58_CR4","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4471-4929-3","volume-title":"Decision Forests for Computer Vision and Medical Image Analysis","author":"A Criminisi","year":"2013","unstructured":"Criminisi, A., Shotton, J.: Decision Forests for Computer Vision and Medical Image Analysis. Springer, London (2013). \nhttps:\/\/doi.org\/10.1007\/978-1-4471-4929-3"},{"issue":"1","key":"58_CR5","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1186\/1471-2105-7-3","volume":"7","author":"R D\u00edaz-Uriarte","year":"2006","unstructured":"D\u00edaz-Uriarte, R., De Andres, S.A.: Gene selection and classification of microarray data using random forest. BMC Bioinform. 7(1), 3 (2006)","journal-title":"BMC Bioinform."},{"key":"58_CR6","unstructured":"Diederik, P., Kingma, J.B.: Adam: a method for stochastic optimization. arXiv preprint \narXiv:1412.6980\n\n (2014)"},{"issue":"1","key":"58_CR7","first-page":"3133","volume":"15","author":"M Fern\u00e1ndez-Delgado","year":"2014","unstructured":"Fern\u00e1ndez-Delgado, M., Cernadas, E., Barro, S., Amorim, D.: Do we need hundreds of classifiers to solve real world classification problems? J. Mach. Learn. Res. 15(1), 3133\u20133181 (2014)","journal-title":"J. Mach. Learn. Res."},{"issue":"6","key":"58_CR8","doi-asserted-by":"publisher","first-page":"838","DOI":"10.1177\/1745691616650285","volume":"11","author":"GM Harari","year":"2016","unstructured":"Harari, G.M., Lane, N.D., Wang, R., Crosier, B.S., Campbell, A.T., Gosling, S.D.: Using smartphones to collect behavioral data in psychological science: opportunities, practical considerations, and challenges. Perspect. Psychol. Sci. 11(6), 838\u2013854 (2016)","journal-title":"Perspect. Psychol. Sci."},{"key":"58_CR9","unstructured":"Kone\u010dn\u1ef3, J., McMahan, H.B., Ramage, D., Richt\u00e1rik, P.: Federated optimization: distributed machine learning for on-device intelligence. arXiv preprint \narXiv:1610.02527\n\n (2016)"},{"key":"58_CR10","unstructured":"Kone\u010dn\u1ef3, J., McMahan, H.B., Yu, F.X., Richt\u00e1rik, P., Suresh, A.T., Bacon, D.: Federated learning: strategies for improving communication efficiency. arXiv preprint \narXiv:1610.05492\n\n (2016)"},{"key":"58_CR11","doi-asserted-by":"crossref","unstructured":"Kontschieder, P., Fiterau, M., Criminisi, A., Rota Bulo, S.: Deep neural decision forests. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1467\u20131475 (2015)","DOI":"10.1109\/ICCV.2015.172"},{"issue":"11","key":"58_CR12","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun, Y., Bottou, L., Bengio, Y., Haffner, P., et al.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998)","journal-title":"Proc. IEEE"},{"key":"58_CR13","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273\u20131282 (2017)"},{"key":"58_CR14","doi-asserted-by":"crossref","unstructured":"Nilsson, A., Smith, S., Ulm, G., Gustavsson, E., Jirstrand, M.: A performance evaluation of federated learning algorithms. In: Proceedings of the Second Workshop on Distributed Infrastructures for Deep Learning (DIDL 2018), New York, NY, USA, vol. 18, pp. 1\u20138 (2018)","DOI":"10.1145\/3286490.3286559"},{"key":"58_CR15","first-page":"1","volume":"22","author":"J Poushter","year":"2016","unstructured":"Poushter, J., et al.: Smartphone ownership and internet usage continues to climb in emerging economies. Pew Res. Cent. 22, 1\u201344 (2016)","journal-title":"Pew Res. Cent."},{"key":"58_CR16","doi-asserted-by":"crossref","unstructured":"Shotton, J., et al.: Real-time human pose recognition in parts from single depth images. In: CVPR, vol. 2, p. 3 (2011)","DOI":"10.1109\/CVPR.2011.5995316"},{"key":"58_CR17","doi-asserted-by":"publisher","first-page":"745","DOI":"10.1007\/978-0-387-84858-7","volume-title":"The Elements of Statistical Learning: Data Mining, Inference, and Prediction","author":"T Hastie","year":"2009","unstructured":"Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction, vol. 2, p. 745. Springer, New York (2009). \nhttps:\/\/doi.org\/10.1007\/978-0-387-84858-7"},{"key":"58_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1007\/978-3-030-16202-3_10","volume-title":"Functional and Constraint Logic Programming","author":"G Ulm","year":"2019","unstructured":"Ulm, G., Gustavsson, E., Jirstrand, M.: Functional federated learning in erlang (ffl-erl). In: Silva, J. (ed.) WFLP 2018. LNCS, vol. 11285, pp. 162\u2013178. Springer, Cham (2019). \nhttps:\/\/doi.org\/10.1007\/978-3-030-16202-3_10"}],"container-title":["Lecture Notes in Computer Science","Machine Learning, Optimization, and Data Science"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-37599-7_58","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,1,3]],"date-time":"2020-01-03T10:03:08Z","timestamp":1578045788000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-37599-7_58"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030375980","9783030375997"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-37599-7_58","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"3 January 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"LOD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Machine Learning, Optimization, and Data Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Siena","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 September 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 September 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mod2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/lod2019.icas.xyz\/","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","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"158","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":"64","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":"0","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":"41% - 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":"5","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":"3","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)"}}]}}