{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T12:18:49Z","timestamp":1791289129662,"version":"4.3.0"},"reference-count":45,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"1","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-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Emerg. Topics Comput."],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1109\/tetc.2024.3474484","type":"journal-article","created":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T17:25:35Z","timestamp":1728581135000},"page":"48-67","source":"Crossref","is-referenced-by-count":21,"title":["FedRDF: A Robust and Dynamic Aggregation Function Against Poisoning Attacks in Federated Learning"],"prefix":"10.1109","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6107-8803","authenticated-orcid":false,"given":"Enrique M\u00e1rmol","family":"Campos","sequence":"first","affiliation":[{"name":"University of Murcia, Murcia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4398-0243","authenticated-orcid":false,"given":"Aurora","family":"Gonzalez-Vidal","sequence":"additional","affiliation":[{"name":"University of Murcia, Murcia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7697-116X","authenticated-orcid":false,"given":"Jos\u00e9 L.","family":"Hern\u00e1ndez-Ramos","sequence":"additional","affiliation":[{"name":"University of Murcia, Murcia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5525-1259","authenticated-orcid":false,"given":"Antonio","family":"Skarmeta","sequence":"additional","affiliation":[{"name":"University of Murcia, Murcia, Spain"}],"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":"10.2501\/IJMR-2017-050"},{"key":"ref3","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1016\/j.ins.2023.03.033","article-title":"Federated learning in smart cities: Privacy and security survey","volume":"632","author":"Rasha","year":"2023","journal-title":"Inf. Sci."},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2022.09.011"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2022.3195054"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2021.108661"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3238823"},{"key":"ref8","first-page":"8635","article-title":"A little is enough: Circumventing defenses for distributed learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Baruch"},{"key":"ref9","article-title":"Intrusion detection based on federated learning: A systematic review","author":"Hernandez-Ramos","year":"2023"},{"key":"ref10","first-page":"634","article-title":"Analyzing federated learning through an adversarial lens","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Bhagoji"},{"key":"ref11","first-page":"5650","article-title":"Byzantine-robust distributed learning: Towards optimal statistical rates","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Yin"},{"key":"ref12","first-page":"118","article-title":"Machine learning with adversaries: Byzantine tolerant gradient descent","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Blanchard"},{"key":"ref13","first-page":"1623","article-title":"Local model poisoning attacks to byzantine-robust federated learning","volume-title":"Proc. 29th USENIX Conf. Secur. Symp.","author":"Fang"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2021.24498"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511977534.020"},{"key":"ref16","article-title":"Mitigating sybils in federated learning poisoning","author":"Fung","year":"2018"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2021.24434"},{"key":"ref18","article-title":"Leaf: A benchmark for federated settings","author":"Caldas","year":"2018"},{"key":"ref19","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/DAC18072.2020.9218663"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2817229"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-007-0064-z"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.21437\/Interspeech.2011-355"},{"key":"ref24","volume-title":"Fourier Series","author":"Tolstov","year":"2012"},{"key":"ref25","volume-title":"A First Course in Fourier Analysis","author":"Kammler","year":"2007"},{"key":"ref26","volume-title":"Fourier Analysis: An Introduction","volume":"1","author":"Stein","year":"2011"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TrustCom56396.2022.00030"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2021.108693"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2020.3041404"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2021.3126728"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2022.3153135"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2021.3128164"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/WorldS450073.2020.9210355"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2021.3135422"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3222842"},{"key":"ref36","article-title":"FastSecAgg: Scalable secure aggregation for privacy-preserving federated learning","author":"Kadhe","year":"2020"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-77428-0"},{"key":"ref38","first-page":"1","article-title":"Fully decentralized federated learning","volume-title":"Proc. 3rd Workshop Bayesian Deep Learn.","author":"Lalitha"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/SP46214.2022.9833647"},{"key":"ref40","first-page":"840","article-title":"Sageflow: Robust federated learning against both stragglers and adversaries","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Park"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.52.5832"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1002\/9781118445112.stat06558"},{"key":"ref43","article-title":"Federated learning priorities under the European Union Artificial Intelligence Act","author":"Woisetschl\u00e4ger","year":"2024"},{"key":"ref44","article-title":"Flower: A friendly federated learning research framework","author":"Beutel","year":"2020"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2017.7966217"}],"container-title":["IEEE Transactions on Emerging Topics in Computing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6245516\/10918563\/10713851.pdf?arnumber=10713851","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,10]],"date-time":"2025-03-10T17:42:12Z","timestamp":1741628532000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10713851\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1]]},"references-count":45,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.1109\/tetc.2024.3474484","relation":{},"ISSN":["2168-6750","2376-4562"],"issn-type":[{"value":"2168-6750","type":"electronic"},{"value":"2376-4562","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1]]}}}