{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T15:32:10Z","timestamp":1784820730554,"version":"3.55.0"},"reference-count":89,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100003093","name":"Fundamental Research Grant Scheme from the Ministry of Higher Education, Malaysia","doi-asserted-by":"publisher","award":["FRGS\/1\/2024\/ICT02\/UM\/01\/1"],"award-info":[{"award-number":["FRGS\/1\/2024\/ICT02\/UM\/01\/1"]}],"id":[{"id":"10.13039\/501100003093","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/access.2025.3611992","type":"journal-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T17:38:25Z","timestamp":1758303505000},"page":"165441-165455","source":"Crossref","is-referenced-by-count":1,"title":["Maverick++: <i>Collaboration-Free<\/i> Unlearning for Medical Privacy Preservation in Healthcare Federated Systems"],"prefix":"10.1109","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-8403-8211","authenticated-orcid":false,"given":"Win","family":"Kent Ong","sequence":"first","affiliation":[{"name":"Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7677-2865","authenticated-orcid":false,"given":"Chee Seng","family":"Chan","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. Artif. Intell. Statist.","author":"McMahan"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2025.3555328"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1145\/3298981"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-57959-7"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.69554\/TCFN5165"},{"key":"ref6","volume-title":"Canadian Privacy: Data Protection and Policy for the Practitioner","author":"Klein","year":"2020"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.11606\/s1518-8787.2022056004461"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/IWQOS52092.2021.9521274"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TETCI.2024.3379240"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2025.3530988"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2024.3486109"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/3679014"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2024.3478334"},{"key":"ref14","article-title":"Exploring federated unlearning: Review, comparison, and insights","author":"Zhao","year":"2023","journal-title":"arXiv:2310.19218"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2023.3297905"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512222"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-53085-2_31"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.004.2300056"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-032-05185-1_35"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2015.35"},{"key":"ref21","article-title":"Machine unlearning: A comprehensive survey","author":"Wang","year":"2024","journal-title":"arXiv:2405.07406"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.32604\/cmc.2023.032307"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.hcc.2024.100254"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/OJCS.2025.3543483"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/s42979-023-01767-4"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.jisa.2025.104010"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/s12559-023-10219-3"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1145\/3460120.3484756"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-45724-2_13"},{"key":"ref30","first-page":"16025","article-title":"Variational Bayesian unlearning","volume-title":"Proc. NeurIPS","volume":"33","author":"Nguyen"},{"key":"ref31","first-page":"1092","article-title":"Machine unlearning for random forests","volume-title":"Proc. ICML","author":"Brophy"},{"key":"ref32","article-title":"Making AI forget you: Data deletion in machine learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Ginart"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2025.3553821"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/SP40001.2021.00019"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/556"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i13.17371"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00750"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i6.25879"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/EuroSP53844.2022.00027"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i11.29092"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW56347.2022.00482"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/137"},{"key":"ref43","article-title":"Unlearnable examples: Making personal data unexploitable","author":"Huang","year":"2021","journal-title":"arXiv:2101.04898"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3266233"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-46677-9_18"},{"key":"ref46","first-page":"1957","article-title":"Towards unbounded machine unlearning","volume-title":"Proc. NeurIPS","author":"Kurmanji"},{"key":"ref47","article-title":"Attack and reset for unlearning: Exploiting adversarial noise toward machine unlearning through parameter re-initialization","author":"Jung","year":"2024","journal-title":"arXiv:2401.08998"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/WACV57701.2024.00475"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00932"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00085"},{"key":"ref51","first-page":"3832","article-title":"Certified data removal from machine learning models","volume-title":"Proc. PMLR","author":"Guo"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2024.3514607"},{"key":"ref53","article-title":"Federated unlearning with knowledge distillation","author":"Wu","year":"2022","journal-title":"arXiv:2201.09441"},{"key":"ref54","first-page":"24150","article-title":"Ferrari: Federated feature unlearning via optimizing feature sensitivity","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Gu"},{"key":"ref55","article-title":"Secure and privacy-preserving surgical instrument segmentation in minimally invasive surgeries using federated differential privacy approach","volume":"125","author":"Bakiya","year":"2025","journal-title":"Comput. Med. Imag. Graph."},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1145\/3539597.3570463"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/SP46215.2023.10179336"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2024.3382321"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1049\/cit2.12281"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM53939.2023.10229075"},{"key":"ref61","article-title":"Federated unlearning: How to efficiently erase a client in FL?","author":"Halimi","year":"2022","journal-title":"arXiv:2207.05521"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3321594"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM48880.2022.9796721"},{"key":"ref64","article-title":"QuickDrop: Efficient federated unlearning by integrated dataset distillation","author":"Dhasade","year":"2023","journal-title":"arXiv:2311.15603"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.001.2300272"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2023.3310049"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2024\/446"},{"key":"ref68","first-page":"14900","article-title":"Anti-backdoor learning: Training clean models on poisoned data","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Li"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.001.2200198"},{"key":"ref70","article-title":"FLIP: A provable defense framework for backdoor mitigation in federated learning","author":"Zhang","year":"2022","journal-title":"arXiv:2210.12873"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1145\/3357713.3384290"},{"key":"ref72","article-title":"Evaluating the robustness of neural networks: An extreme value theory approach","volume-title":"Proc. ICLR","author":"Weng"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1007\/s10107-024-02143-7"},{"key":"ref74","first-page":"18075","article-title":"Remember what you want to forget: Algorithms for machine unlearning","volume-title":"Proc. NeurIPS","author":"Sekhari"},{"key":"ref75","first-page":"931","article-title":"Descent-to-delete: Gradient-based methods for machine unlearning","volume-title":"Proc. 32nd Int. Conf. Algorithmic Learn. Theory","author":"Neel"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.29012\/jpc.924"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1002\/9781119229070.ch12"},{"key":"ref78","article-title":"PyTorch: An imperative style, high-performance deep learning library","volume-title":"Proc. NeurIPS","author":"Paszke"},{"key":"ref79","first-page":"2938","article-title":"How to backdoor federated learning","volume-title":"Proc. AISTATS","author":"Bagdasaryan"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2023.3328269"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-022-01721-8"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1109\/SP46214.2022.9833649"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.74"},{"issue":"86","key":"ref85","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref86","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"ref87","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.1145\/2810103.2813677"},{"key":"ref89","doi-asserted-by":"publisher","DOI":"10.1145\/3243734.3243834"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/10820123\/11173580.pdf?arnumber=11173580","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T14:38:34Z","timestamp":1759243114000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11173580\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":89,"URL":"https:\/\/doi.org\/10.1109\/access.2025.3611992","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}