{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T22:43:20Z","timestamp":1773441800657,"version":"3.50.1"},"reference-count":50,"publisher":"IEEE","license":[{"start":{"date-parts":[[2024,12,15]],"date-time":"2024-12-15T00:00:00Z","timestamp":1734220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,12,15]],"date-time":"2024-12-15T00:00:00Z","timestamp":1734220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,12,15]]},"DOI":"10.1109\/bigdata62323.2024.10825564","type":"proceedings-article","created":{"date-parts":[[2025,1,16]],"date-time":"2025-01-16T18:31:23Z","timestamp":1737052283000},"page":"5068-5077","source":"Crossref","is-referenced-by-count":4,"title":["Explainable Vertical Federated Learning for Healthcare: Ensuring Privacy and Optimal Accuracy"],"prefix":"10.1109","author":[{"given":"Shahnewaz Karim","family":"Sakib","sequence":"first","affiliation":[{"name":"University of Tennessee at Chattanooga,Computer Science and Engineering,Chattanooga,TN,USA,37403"}]},{"given":"Anindya Bijoy","family":"Das","sequence":"additional","affiliation":[{"name":"The University of Akron,Electrical and Computer Engineering,Akron,OH,USA,44325"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.116912"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2024.3442012"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2021.3097237"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2024.3352628"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2022.3178443"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/GLOBECOM54140.2023.10437633"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2023.3309701"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3074676"},{"key":"ref9","first-page":"8227","article-title":"Accuracy, interpretability, and differential privacy via explainable boosting","volume-title":"Intl. Conf. Mach. Learn. (ICML)","author":"Nori"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-29726-8_2"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/3579363"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/SaTML54575.2023.00038"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.3390\/s23020634"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.03.008"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-020-0186-1"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.104130"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/3514221.3526127"},{"key":"ref18","first-page":"20 296","article-title":"FedVS: Straggler-resilient and privacy-preserving vertical federated learning for split models","volume-title":"Intl. Conf. Mach. Learn. (ICML)","author":"Li"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/SP46215.2023.10179422"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICC42927.2021.9500401"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/3624982"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CSF57540.2023.00007"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2024.3400608"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.sysarc.2022.102474"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.14778\/3603581.3603588"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2022.3188292"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1145\/3561048"},{"key":"ref28","article-title":"A unified approach to interpreting model predictions","volume-title":"Intl. Conf. on Neural Inf. Process. Syst. (NeurIPS)","author":"Lundberg"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2020.03.039"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.106848"},{"key":"ref31","first-page":"2088","article-title":"VF-PS: How to select important participants in vertical federated learning, efficiently and securely?","volume-title":"Adv. Neural Inf. Process. Syst. (NeurIPS)","volume":"35","author":"Jiang"},{"key":"ref32","first-page":"994","article-title":"Cafe: Catastrophic data leakage in vertical federated learning","volume-title":"Adv. Neural Inf. Process. Syst. (NeurIPS)","volume":"34","author":"Jin"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3072238"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1145\/1536414.1536440"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/11681878_14"},{"key":"ref36","article-title":"Classification of EEG signals using normal inverse gaussian parameters in the DTCWT domain for seizure detection","volume":"10","author":"Das","year":"2016","journal-title":"Sig. Img. Vid. Process"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.3390\/bdcc7010010"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.3389\/frai.2021.752558"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2021.3139055"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1145\/3678181"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2018.2809005"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2019.2962804"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/ISIT.2018.8437735"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2019.2934414"},{"key":"ref45","article-title":"Evaluating explainability for machine learning predictions using model-agnostic metrics","author":"Munoz","year":"2023","journal-title":"preprint arXiv:2302.12094"},{"key":"ref46","article-title":"Representativity and consistency measures for deep neural network explanations","author":"Fel","year":"2020","journal-title":"preprint arXiv:2009.04521"},{"key":"ref47","article-title":"On quantitative aspects of model interpretability","author":"Nguyen","year":"2020","journal-title":"preprint arXiv:2007.07584"},{"key":"ref48","first-page":"18 770","article-title":"A consistent and efficient evaluation strategy for attribution methods","volume-title":"Intl. Conf. Mach. Learn. (ICML)","author":"Rong"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177729694"},{"key":"ref50","volume-title":"Diabetes Health Indicators Dataset"}],"event":{"name":"2024 IEEE International Conference on Big Data (BigData)","location":"Washington, DC, USA","start":{"date-parts":[[2024,12,15]]},"end":{"date-parts":[[2024,12,18]]}},"container-title":["2024 IEEE International Conference on Big Data (BigData)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10824975\/10824942\/10825564.pdf?arnumber=10825564","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,17]],"date-time":"2025-01-17T07:47:21Z","timestamp":1737100041000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10825564\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,15]]},"references-count":50,"URL":"https:\/\/doi.org\/10.1109\/bigdata62323.2024.10825564","relation":{},"subject":[],"published":{"date-parts":[[2024,12,15]]}}}