{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T11:43:22Z","timestamp":1770464602493,"version":"3.49.0"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031185229","type":"print"},{"value":"9783031185236","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-18523-6_14","type":"book-chapter","created":{"date-parts":[[2022,10,8]],"date-time":"2022-10-08T10:04:48Z","timestamp":1665223488000},"page":"141-151","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Towards Sparsified Federated Neuroimaging Models via\u00a0Weight Pruning"],"prefix":"10.1007","author":[{"given":"Dimitris","family":"Stripelis","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Umang","family":"Gupta","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nikhil","family":"Dhinagar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Greg Ver","family":"Steeg","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paul M.","family":"Thompson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jos\u00e9 Luis","family":"Ambite","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,10,7]]},"reference":[{"key":"14_CR1","doi-asserted-by":"crossref","unstructured":"Bibikar, S., Vikalo, H., Wang, Z., Chen, X.: Federated dynamic sparse training: computing less, communicating less, yet learning better (2021)","DOI":"10.1609\/aaai.v36i6.20555"},{"key":"14_CR2","doi-asserted-by":"crossref","unstructured":"Cole, J.H., Leech, R., Sharp, D.J., Alzheimer\u2019s Disease Neuroimaging Initiative: Prediction of brain age suggests accelerated atrophy after traumatic brain injury. Ann. Neurol. 77(4), 571\u2013581 (2015)","DOI":"10.1002\/ana.24367"},{"issue":"10","key":"14_CR3","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1038\/s41591-021-01506-3","volume":"27","author":"I Dayan","year":"2021","unstructured":"Dayan, I., et al.: Federated learning for predicting clinical outcomes in patients with Covid-19. Nat. Med. 27(10), 1735\u20131743 (2021)","journal-title":"Nat. Med."},{"issue":"11","key":"14_CR4","doi-asserted-by":"publisher","first-page":"1855","DOI":"10.1002\/alz.12491","volume":"17","author":"A Ezzati","year":"2021","unstructured":"Ezzati, A., et al.: Predictive value of ATN biomarker profiles in estimating disease progression in Alzheimer\u2019s disease dementia. Alzheimer\u2019s & Dementia 17(11), 1855\u20131867 (2021)","journal-title":"Alzheimer\u2019s & Dementia"},{"key":"14_CR5","unstructured":"Farokhi, F., Kaafar, M.A.: Modelling and quantifying membership information leakage in machine learning. arXiv preprint arXiv:2001.10648 (2020)"},{"key":"14_CR6","unstructured":"Frankle, J., Carbin, M.: The lottery ticket hypothesis: finding sparse, trainable neural networks. In: International Conference on Learning Representations (2018)"},{"key":"14_CR7","doi-asserted-by":"crossref","unstructured":"Gupta, U., Lam, P.K., Ver Steeg, G., Thompson, P.M.: Improved brain age estimation with slice-based set networks. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 840\u2013844. IEEE (2021)","DOI":"10.1109\/ISBI48211.2021.9434081"},{"key":"14_CR8","unstructured":"Gupta, U., Stripelis, D., Lam, P.K., Thompson, P., Ambite, J.L., Ver Steeg, G.: Membership inference attacks on deep regression models for neuroimaging. In: Medical Imaging with Deep Learning, pp. 228\u2013251. PMLR (2021)"},{"issue":"241","key":"14_CR9","first-page":"1","volume":"22","author":"T Hoefler","year":"2021","unstructured":"Hoefler, T., Alistarh, D., Ben-Nun, T., Dryden, N., Peste, A.: Sparsity in deep learning: pruning and growth for efficient inference and training in neural networks. J. Mach. Learn. Res. 22(241), 1\u2013124 (2021)","journal-title":"J. Mach. Learn. Res."},{"key":"14_CR10","doi-asserted-by":"crossref","unstructured":"Jayaraman, B., Wang, L., Evans, D., Gu, Q.: Revisiting membership inference under realistic assumptions. arXiv preprint arXiv:2005.10881 (2020)","DOI":"10.2478\/popets-2021-0031"},{"key":"14_CR11","unstructured":"Jha, S.K., et al.: An extension of Fano\u2019s inequality for characterizing model susceptibility to membership inference attacks. arXiv preprint arXiv:2009.08097 (2020)"},{"key":"14_CR12","doi-asserted-by":"crossref","unstructured":"Jiang, Y., et al.: Model pruning enables efficient federated learning on edge devices. IEEE Trans. Neural Netw. Learn. Syst. (2022)","DOI":"10.1109\/TNNLS.2022.3166101"},{"issue":"1","key":"14_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-019-13163-9","volume":"10","author":"BA J\u00f3nsson","year":"2019","unstructured":"J\u00f3nsson, B.A., et al.: Brain age prediction using deep learning uncovers associated sequence variants. Nat. Commun. 10(1), 1\u201310 (2019)","journal-title":"Nat. Commun."},{"key":"14_CR14","unstructured":"Kurtz, M., et al.: Inducing and exploiting activation sparsity for fast inference on deep neural networks. In: International Conference on Machine Learning, pp. 5533\u20135543. PMLR (2020)"},{"key":"14_CR15","doi-asserted-by":"crossref","unstructured":"Lam, P.K., et al.: Accurate brain age prediction using recurrent slice-based networks. In: 16th International Symposium on Medical Information Processing and Analysis, vol. 11583, p. 1158303. International Society for Optics and Photonics (2020)","DOI":"10.1117\/12.2579630"},{"issue":"3","key":"14_CR16","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1109\/MSP.2020.2975749","volume":"37","author":"T Li","year":"2020","unstructured":"Li, T., Sahu, A.K., Talwalkar, A., Smith, V.: Federated learning: challenges, methods, and future directions. IEEE Signal Process. Mag. 37(3), 50\u201360 (2020)","journal-title":"IEEE Signal Process. Mag."},{"key":"14_CR17","unstructured":"Liu, Z., Sun, M., Zhou, T., Huang, G., Darrell, T.: Rethinking the value of network pruning. In: International Conference on Learning Representations (2018)"},{"key":"14_CR18","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. PMLR (2017)"},{"issue":"11","key":"14_CR19","doi-asserted-by":"publisher","first-page":"1523","DOI":"10.1038\/nn.4393","volume":"19","author":"KL Miller","year":"2016","unstructured":"Miller, K.L., et al.: Multimodal population brain imaging in the UK biobank prospective epidemiological study. Nat. Neurosci. 19(11), 1523\u20131536 (2016)","journal-title":"Nat. Neurosci."},{"key":"14_CR20","doi-asserted-by":"crossref","unstructured":"Nasr, M., Shokri, R., Houmansadr, A.: Comprehensive privacy analysis of deep learning: passive and active white-box inference attacks against centralized and federated learning. In: IEEE Symposium on Security and Privacy (SP) (2019)","DOI":"10.1109\/SP.2019.00065"},{"key":"14_CR21","doi-asserted-by":"crossref","unstructured":"Nasr, M., Shokri, R., Houmansadr, A.: Machine learning with membership privacy using adversarial regularization. In: Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, pp. 634\u2013646 (2018)","DOI":"10.1145\/3243734.3243855"},{"key":"14_CR22","doi-asserted-by":"crossref","unstructured":"Peng, H., Gong, W., Beckmann, C.F., Vedaldi, A., Smith, S.M.: Accurate brain age prediction with lightweight deep neural networks. Med. Image Anal. 68, 101871 (2021)","DOI":"10.1016\/j.media.2020.101871"},{"key":"14_CR23","doi-asserted-by":"crossref","unstructured":"Pustozerova, A., Mayer, R.: Information leaks in federated learning. In: Proceedings of the Network and Distributed System Security Symposium, vol. 10 (2020)","DOI":"10.14722\/diss.2020.23004"},{"issue":"1","key":"14_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41746-020-00323-1","volume":"3","author":"N Rieke","year":"2020","unstructured":"Rieke, N., et al.: The future of digital health with federated learning. NPJ Digit. Med. 3(1), 1\u20137 (2020)","journal-title":"NPJ Digit. Med."},{"key":"14_CR25","doi-asserted-by":"crossref","unstructured":"Ro, J.H., et al.: Scaling language model size in cross-device federated learning. In: ACL Workshop on Federated Learning for Natural Language Processing (2022)","DOI":"10.18653\/v1\/2022.fl4nlp-1.2"},{"issue":"1","key":"14_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-69250-1","volume":"10","author":"MJ Sheller","year":"2020","unstructured":"Sheller, M.J., et al.: Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data. Sci. Rep. 10(1), 1\u201312 (2020)","journal-title":"Sci. Rep."},{"key":"14_CR27","doi-asserted-by":"crossref","unstructured":"Stripelis, D., Ambite, J.L., Lam, P., Thompson, P.: Scaling neuroscience research using federated learning. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 1191\u20131195. IEEE (2021)","DOI":"10.1109\/ISBI48211.2021.9433925"},{"key":"14_CR28","unstructured":"Stripelis, D., Gupta, U., Steeg, G.V., Ambite, J.L.: Federated progressive sparsification (purge, merge, tune)+. arXiv preprint arXiv:2204.12430 (2022)"},{"key":"14_CR29","doi-asserted-by":"crossref","unstructured":"Stripelis, D., Thompson, P.M., Ambite, J.L.: Semi-synchronous federated learning for energy-efficient training and accelerated convergence in cross-silo settings. ACM Trans. Intell. Syst. Technol. (TIST) (2022)","DOI":"10.1145\/3524885"},{"key":"14_CR30","unstructured":"Truex, S., Liu, L., Gursoy, M.E., Yu, L., Wei, W.: Towards demystifying membership inference attacks. arXiv preprint arXiv:1807.09173 (2018)"},{"issue":"9","key":"14_CR31","doi-asserted-by":"publisher","first-page":"829","DOI":"10.1038\/nbt.4233","volume":"36","author":"M Wainberg","year":"2018","unstructured":"Wainberg, M., Merico, D., Delong, A., Frey, B.J.: Deep learning in biomedicine. Nat. Biotechnol. 36(9), 829\u2013838 (2018)","journal-title":"Nat. Biotechnol."},{"issue":"3","key":"14_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-031-01585-4","volume":"13","author":"Q Yang","year":"2019","unstructured":"Yang, Q., Liu, Y., Cheng, Y., Kang, Y., Chen, T., Yu, H.: Federated learning. Synthesis Lectures Artif. Intell. Mach. Learn. 13(3), 1\u2013207 (2019)","journal-title":"Synthesis Lectures Artif. Intell. Mach. Learn."},{"key":"14_CR33","unstructured":"Zari, O., Xu, C., Neglia, G.: Efficient passive membership inference attack in federated learning. In: NeurIPS PriML Workshop (2021)"},{"issue":"7697","key":"14_CR34","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1038\/nature25988","volume":"555","author":"B Zhu","year":"2018","unstructured":"Zhu, B., Liu, J.Z., Cauley, S.F., Rosen, B.R., Rosen, M.S.: Image reconstruction by domain-transform manifold learning. Nature 555(7697), 487\u2013492 (2018)","journal-title":"Nature"},{"key":"14_CR35","unstructured":"Zhu, M., Gupta, S.: To prune, or not to prune: exploring the efficacy of pruning for model compression. arXiv preprint arXiv:1710.01878 (2017)"}],"container-title":["Lecture Notes in Computer Science","Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Diverse Global Health"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-18523-6_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,8]],"date-time":"2022-10-08T10:08:03Z","timestamp":1665223683000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-18523-6_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031185229","9783031185236"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-18523-6_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"7 October 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DeCaF","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Distributed, Collaborative, and Federated Learning","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"decaf2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/decaf-workshop.github.io\/decaf-2022\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"18","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":"14","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":"78% - 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":"3","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}