{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T16:11:35Z","timestamp":1770739895190,"version":"3.49.0"},"reference-count":59,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"3","license":[{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"SNS JU","award":["101192521"],"award-info":[{"award-number":["101192521"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. on Mobile Comput."],"published-print":{"date-parts":[[2026,3]]},"DOI":"10.1109\/tmc.2025.3619560","type":"journal-article","created":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:55:54Z","timestamp":1760032554000},"page":"3889-3904","source":"Crossref","is-referenced-by-count":0,"title":["Achieving Machine Learning Dependability Through Model Switching and Compression"],"prefix":"10.1109","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0458-2004","authenticated-orcid":false,"given":"Francesco","family":"Malandrino","sequence":"first","affiliation":[{"name":"CNR-IEIIT, Torino, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9990-512X","authenticated-orcid":false,"given":"Giacomo","family":"Di Giuseppe","sequence":"additional","affiliation":[{"name":"Politecnico di Torino, Torino, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6920-4189","authenticated-orcid":false,"given":"Marco","family":"Levorato","sequence":"additional","affiliation":[{"name":"University of California, Irvine, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1410-660X","authenticated-orcid":false,"given":"Carla Fabiana","family":"Chiasserini","sequence":"additional","affiliation":[{"name":"CNR-IEIIT, CNIT, Torino, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3055523"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/3552326.3567485"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2019.2904348"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TCCN.2021.3084406"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM53939.2023.10229076"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01547"},{"key":"ref7","article-title":"Parameter-efficient fine-tuning for large models: A comprehensive survey","author":"Han","year":"2024"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.197"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"ref10","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref11","article-title":"To prune, or not to prune: Exploring the efficacy of pruning for model compression","author":"Zhu","year":"2017"},{"key":"ref12","first-page":"129","article-title":"What is the state of neural network pruning?","volume-title":"Proc. Mach. Learn. Syst.","volume":"2","author":"Blalock"},{"key":"ref13","first-page":"93","article-title":"Lost in pruning: The effects of pruning neural networks beyond test accuracy","volume-title":"Proc. Mach. Learn. Syst.","volume":"3","author":"Liebenwein"},{"key":"ref14","article-title":"Uncertainty sets for image classifiers using conformal prediction","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Angelopoulos"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-05318-5_3"},{"key":"ref16","article-title":"Federated optimization: Distributed optimization beyond the datacenter","author":"Kone\u010dn\u00fd","year":"2015"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM42981.2021.9488723"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM41043.2020.9155494"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2019.8737400"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/NetSoft54395.2022.9844093"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-15712-8_59"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.97"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/s10107-006-0084-2"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/BF02579273"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-68874-4_10"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/s10107-007-0189-2"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-72792-7_21"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2021.3120318"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2006.890089"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2017.8057039"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-24777-7_16"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2011.6033395"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref34","first-page":"169","article-title":"On the convergence of FedAvg on non-IID data","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Li"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1145\/3377454"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.001.2001016"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP48485.2024.10447377"},{"key":"ref38","article-title":"Leader-follower neural networks with local error signals inspired by complex collectives","author":"Yin","year":"2023"},{"key":"ref39","first-page":"15070","article-title":"Personalized federated learning through local memorization","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Marfoq"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2021.3090331"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA51294.2020.00185"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2022.3222640"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP43922.2022.9746093"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2019.8737602"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/ISIT50566.2022.9834840"},{"key":"ref46","first-page":"9356","article-title":"DropNet: Reducing neural network complexity via iterative pruning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Tan"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01237-3_12"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-021-01453-z"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/ICInfA.2018.8812321"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/IROS51168.2021.9636206"},{"key":"ref51","article-title":"Robust federated learning in a heterogeneous environment","author":"Ghosh","year":"2019"},{"key":"ref52","first-page":"6357","article-title":"Ditto: Fair and robust federated learning through personalization","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Li"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/TCOMM.2020.2979149"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2944481"},{"key":"ref55","article-title":"Learning to detect malicious clients for robust federated learning","author":"Li","year":"2020"},{"key":"ref56","article-title":"Conformal risk control","author":"Angelopoulos","year":"2022"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2025.2506198"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1093\/jrsssb\/qkae114"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/WoWMoM60985.2024.00036"}],"container-title":["IEEE Transactions on Mobile Computing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/7755\/11372515\/11197303.pdf?arnumber=11197303","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T21:10:07Z","timestamp":1770671407000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11197303\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3]]},"references-count":59,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.1109\/tmc.2025.3619560","relation":{},"ISSN":["1536-1233","1558-0660","2161-9875"],"issn-type":[{"value":"1536-1233","type":"print"},{"value":"1558-0660","type":"electronic"},{"value":"2161-9875","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3]]}}}