{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T17:59:45Z","timestamp":1778608785728,"version":"3.51.4"},"reference-count":65,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"5","license":[{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100000780","name":"European Commission \u201cSoBigData++\u201d","doi-asserted-by":"publisher","award":["H2020-871042"],"award-info":[{"award-number":["H2020-871042"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000780","name":"\u201cMobiDataLab\u201d","doi-asserted-by":"publisher","award":["H2020-101006879"],"award-info":[{"award-number":["H2020-101006879"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Ministerio de Ciencia e Innovaci\u00f3n (MCIN)\/Agencia Estatal de Investigaci\u00f3n","award":["10.13039\/501100011033"],"award-info":[{"award-number":["10.13039\/501100011033"]}]},{"DOI":"10.13039\/501100004837","name":"\u201cEuropean Regional Development Fund (ERDF) a Way of Making Europe\u201d","doi-asserted-by":"publisher","award":["PID2021-123637NB-I00 \u201cCURLING.\u201d"],"award-info":[{"award-number":["PID2021-123637NB-I00 \u201cCURLING.\u201d"]}],"id":[{"id":"10.13039\/501100004837","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002943","name":"FI","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002943","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002943","name":"Government of Catalonia [Instituci\u00f3 Catalana de Recerca i Estudis Avan\u00e7ats (ICREA) Acad\u00e8mia Prizes]","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002943","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Neural Netw. Learning Syst."],"published-print":{"date-parts":[[2024,5]]},"DOI":"10.1109\/tnnls.2022.3212627","type":"journal-article","created":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T21:37:37Z","timestamp":1667511457000},"page":"6703-6717","source":"Crossref","is-referenced-by-count":37,"title":["Enhanced Security and Privacy via Fragmented Federated Learning"],"prefix":"10.1109","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4911-3802","authenticated-orcid":false,"given":"Najeeb Moharram","family":"Jebreel","sequence":"first","affiliation":[{"name":"Department of Computer Engineering and Mathematics, UNESCO Chair in Data Privacy, CYBERCAT Center for Cybersecurity Research of Catalonia, Universitat Rovira i Virgili, Tarragona, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7213-4962","authenticated-orcid":false,"given":"Josep","family":"Domingo-Ferrer","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering and Mathematics, UNESCO Chair in Data Privacy, CYBERCAT Center for Cybersecurity Research of Catalonia, Universitat Rovira i Virgili, Tarragona, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1108-8082","authenticated-orcid":false,"given":"Alberto","family":"Blanco-Justicia","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering and Mathematics, UNESCO Chair in Data Privacy, CYBERCAT Center for Cybersecurity Research of Catalonia, Universitat Rovira i Virgili, Tarragona, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7275-7887","authenticated-orcid":false,"given":"David","family":"S\u00e1nchez","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering and Mathematics, UNESCO Chair in Data Privacy, CYBERCAT Center for Cybersecurity Research of Catalonia, Universitat Rovira i Virgili, Tarragona, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. 20th Int. Conf. Artif. Intell. Statist.","author":"McMahan"},{"key":"ref2","article-title":"Towards federated learning at scale: System design","author":"Bonawitz","year":"2019","journal-title":"arXiv:1902.01046"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.376"},{"key":"ref4","article-title":"Protection against reconstruction and its applications in private federated learning","author":"Bhowmick","year":"2018","journal-title":"arXiv:1812.00984"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2018.01.007"},{"key":"ref6","article-title":"Federated learning for mobile keyboard prediction","author":"Hard","year":"2018","journal-title":"arXiv:1811.03604"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3077803"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-57959-7"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1561\/9781680837896"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2020.10.007"},{"key":"ref11","article-title":"Learning differentially private recurrent language models","author":"Brendan McMahan","year":"2017","journal-title":"arXiv:1710.06963"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00065"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00029"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-63076-8_2"},{"key":"ref15","article-title":"IDLG: Improved deep leakage from gradients","author":"Zhao","year":"2020","journal-title":"arXiv:2001.02610"},{"key":"ref16","article-title":"Inverting gradients\u2014How easy is it to break privacy in federated learning?","author":"Geiping","year":"2020","journal-title":"arXiv:2003.14053"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2021.104468"},{"key":"ref18","first-page":"118","article-title":"Machine learning with adversaries: Byzantine tolerant gradient descent","volume-title":"Proc. 31st Int. Conf. Neural Inf. Process. Syst.","author":"Blanchard"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2020.3012952"},{"key":"ref20","article-title":"Poisoning attacks against support vector machines","author":"Biggio","year":"2012","journal-title":"arXiv:1206.6389"},{"key":"ref21","first-page":"301","article-title":"The limitations of federated learning in sybil settings","volume-title":"Proc. 23rd Int. Symp. Res. Attacks, Intrusions Defenses (RAID)","author":"Fung"},{"key":"ref22","first-page":"2938","article-title":"How to backdoor federated learning","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Bagdasaryan"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1561\/9781601988195"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1145\/1536414.1536440"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/SFCS.1982.38"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/3433638"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1145\/3547139"},{"key":"ref28","first-page":"5650","article-title":"Byzantine-robust distributed learning: Towards optimal statistical rates","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Yin"},{"key":"ref29","article-title":"Mitigating sybils in federated learning poisoning","author":"Fung","year":"2018","journal-title":"arXiv:1808.04866"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.2975749"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1016\/j.ensm.2020.06.033"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3133982"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/JSAIT.2021.3054610"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2017.2787987"},{"key":"ref35","article-title":"Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption","author":"Hardy","year":"2017","journal-title":"arXiv:1711.10677"},{"key":"ref36","first-page":"493","article-title":"BatchCrypt: Efficient homomorphic encryption for cross-silo federated learning","volume-title":"Proc. USENIX Annu. Tech. Conf.","author":"Zhang"},{"key":"ref37","article-title":"Differentially private federated learning: A client level perspective","author":"Geyer","year":"2017","journal-title":"arXiv:1712.07557"},{"key":"ref38","article-title":"The hidden vulnerability of distributed learning in byzantium","author":"Mahdi El Mhamdi","year":"2018","journal-title":"arXiv:1802.07927"},{"key":"ref39","first-page":"903","article-title":"Draco: Byzantine-resilient distributed training via redundant gradients","volume-title":"Proc. 35th Int. Conf. Mach. Learn.","author":"Chen"},{"key":"ref40","article-title":"Cronus: Robust and heterogeneous collaborative learning with black-box knowledge transfer","author":"Chang","year":"2019","journal-title":"arXiv:1912.11279"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2022.23054"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2021.3108434"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2022.3169918"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2020.3041404"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2022.3167434"},{"key":"ref46","article-title":"SAFELearning: Enable backdoor detectability in federated learning with secure aggregation","author":"Zhang","year":"2021","journal-title":"arXiv:2102.02402"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3102155"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2020.02.037"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1145\/3457388.3458665"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1145\/3243734.3243834"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3134012"},{"key":"ref52","first-page":"1605","article-title":"Local model poisoning attacks to Byzantine-robust federated learning","volume-title":"Proc. 29th USENIX Secur. Symp. (USENIX Secur.)","author":"Fang"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2018.00057"},{"key":"ref54","first-page":"634","article-title":"Analyzing federated learning through an adversarial lens","volume-title":"Proc. 36th Int. Conf. Mach. Learn.","author":"Bhagoji"},{"key":"ref55","article-title":"Poison frogs! Targeted clean-label poisoning attacks on neural networks","author":"Shafahi","year":"2018","journal-title":"arXiv:1804.00792"},{"key":"ref56","volume-title":"Gboard Passes One Billion Installs on the Play Store","author":"Davenport","year":"2018"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33011544"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1976.1055638"},{"key":"ref59","author":"Lepinski","year":"5114","journal-title":"Additional Diffie-Hellman Groups for Use With IETF Standards"},{"key":"ref60","volume-title":"PyCryptodome a Self-Contained Python Package of Low-Level Cryptographic Primitives","year":"2022"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-46805-6_19"},{"key":"ref62","volume-title":"Learning Multiple Layers of Features From Tiny Images","author":"Krizhevsky","year":"2009"},{"key":"ref63","volume-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","year":"2014"},{"key":"ref64","first-page":"142","article-title":"Learning word vectors for sentiment analysis","volume-title":"Proc. 49th Annu. Meeting Assoc. Comput. Linguistics: Hum. Lang. Technol.","author":"Maas"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/sp46214.2022.9833647"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/5962385\/10517792\/09925189.pdf?arnumber=9925189","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,3]],"date-time":"2024-05-03T18:50:05Z","timestamp":1714762205000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9925189\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5]]},"references-count":65,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2022.3212627","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"value":"2162-237X","type":"print"},{"value":"2162-2388","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5]]}}}