{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T16:05:25Z","timestamp":1781885125000,"version":"3.54.5"},"reference-count":57,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"5","license":[{"start":{"date-parts":[[2023,10,1]],"date-time":"2023-10-01T00:00:00Z","timestamp":1696118400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100000780","name":"European Commission","doi-asserted-by":"publisher","award":["10109571"],"award-info":[{"award-number":["10109571"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Ministry of Innovation and Technology"},{"DOI":"10.13039\/501100012550","name":"Nemzeti Kutat\u00e1si, Fejleszt\u00e9si \u00e9s Innovaci\u00f3s Alap","doi-asserted-by":"publisher","award":["138903"],"award-info":[{"award-number":["138903"]}],"id":[{"id":"10.13039\/501100012550","id-type":"DOI","asserted-by":"publisher"}]},{"name":"FK_21 funding scheme"},{"name":"Ministry of Culture and Innovation of Hungary"},{"DOI":"10.13039\/501100012550","name":"Nemzeti Kutat\u00e1si, Fejleszt\u00e9si \u00e9s Innovaci\u00f3s Alap","doi-asserted-by":"publisher","award":["TKP2021-NVA-02"],"award-info":[{"award-number":["TKP2021-NVA-02"]}],"id":[{"id":"10.13039\/501100012550","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012550","name":"Nemzeti Kutat\u00e1si, Fejleszt\u00e9si \u00e9s Innovaci\u00f3s Alap","doi-asserted-by":"publisher","award":["TKP2021-NVA"],"award-info":[{"award-number":["TKP2021-NVA"]}],"id":[{"id":"10.13039\/501100012550","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Big Data"],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1109\/tbdata.2023.3280406","type":"journal-article","created":{"date-parts":[[2023,5,29]],"date-time":"2023-05-29T17:42:12Z","timestamp":1685382132000},"page":"1430-1437","source":"Crossref","is-referenced-by-count":21,"title":["Quality Inference in Federated Learning With Secure Aggregation"],"prefix":"10.1109","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1825-9251","authenticated-orcid":false,"given":"Bal\u00e1zs","family":"Pej\u00f3","sequence":"first","affiliation":[{"name":"CrySyS Lab, Department of Networked Systems and Services, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Budapest, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3891-3855","authenticated-orcid":false,"given":"Gergely","family":"Bicz\u00f3k","sequence":"additional","affiliation":[{"name":"CrySyS Lab, Department of Networked Systems and Services, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Budapest, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref13","first-page":"2938","article-title":"How to backdoor federated learning","author":"bagdasaryan","year":"2020","journal-title":"Proc Int Conf Artif Intell Statist"},{"key":"ref57","article-title":"Semi-supervised knowledge transfer for deep learning from private training data","author":"papernot","year":"2016"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1142\/S0218488502001648"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2019.8737416"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-63076-8_11"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24106-7"},{"key":"ref53","article-title":"Measuring contributions in privacy-preserving federated learning","volume":"2021","author":"pej\u00f3","year":"2021","journal-title":"ERCIM News"},{"key":"ref52","first-page":"11422","article-title":"Data appraisal without data sharing","author":"xu","year":"0","journal-title":"Proc Int Conf Artif Intell Statist"},{"key":"ref11","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"mcmahan","year":"2016"},{"key":"ref55","article-title":"Eavesdrop the composition proportion of training labels in federated learning","author":"wang","year":"2019"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.2478\/popets-2020-0028"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM51629.2021.00129"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1176343414"},{"key":"ref16","article-title":"The good, the bad and the ugly","year":"1966"},{"key":"ref19","article-title":"Federated learning in adversarial settings","author":"kerkouche","year":"2020"},{"key":"ref18","author":"ludeman","year":"2003","journal-title":"Random Processes Filtering Estimation and Detection"},{"key":"ref51","article-title":"Toward understanding the influence of individual clients in federated learning","author":"xue","year":"2020"},{"key":"ref50","article-title":"Understanding black-box predictions via influence functions","author":"koh","year":"2017"},{"key":"ref46","article-title":"A distributional framework for data valuation","author":"ghorbani","year":"2020","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i10.17093"},{"key":"ref48","article-title":"GTG-Shapley: Efficient and accurate participant contribution evaluation in federated learning","author":"liu","year":"2021"},{"key":"ref47","article-title":"Beta Shapley: A unified and noise-reduced data valuation framework for machine learning","author":"kwon","year":"2021"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TPS-ISA50397.2020.00014"},{"key":"ref41","article-title":"Secure weighted aggregation for federated learning","author":"guo","year":"2020"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1016\/j.cor.2008.04.004"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/778"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2022.3198733"},{"key":"ref8","first-page":"601","article-title":"Stealing machine learning models via prediction APIs","author":"tram\u00e8r","year":"2016","journal-title":"Proc 25th USENIX Secur Symp"},{"key":"ref7","first-page":"14747","article-title":"Deep leakage from gradients","author":"zhu","year":"2019","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00029"},{"key":"ref4","article-title":"Federated learning: Strategies for improving communication efficiency","author":"kone?n\u00fd","year":"2016"},{"key":"ref3","author":"gentry","year":"2009","journal-title":"A Fully Homomorphic Encryption Scheme"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.41"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/2810103.2813677"},{"key":"ref40","article-title":"Focus: Dealing with label quality disparity in federated learning","author":"chen","year":"2020"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611974560"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1145\/1871437.1871557"},{"key":"ref37","first-page":"1846","article-title":"Free-rider attacks on model aggregation in federated learning","author":"fraboni","year":"2021","journal-title":"Proc Int Conf Artif Intell Statist"},{"key":"ref36","article-title":"Free-riders in federated learning: Attacks and defenses","author":"lin","year":"2019"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1145\/773153.773173"},{"key":"ref30","article-title":"Mitigating sybils in federated learning poisoning","author":"fung","year":"2018"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2011.2165054"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-45209-6_171"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781107337756"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.2478\/popets-2019-0019"},{"key":"ref39","article-title":"Securing secure aggregation: Mitigating multi-round privacy leakage in federated learning","author":"so","year":"2021"},{"key":"ref38","article-title":"FedCM: A real-time contribution measurement method for participants in federated learning","author":"liu","year":"2021"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1145\/358549.358563"},{"key":"ref23","article-title":"The CIFAR-10 dataset","author":"krizhevsky","year":"2014"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.4086\/toc.2012.v008a006"},{"key":"ref25","article-title":"Differentially private federated learning: A client level perspective","author":"geyer","year":"2017"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1002\/0470011815.b2a15150"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2012.2211477"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1977.tb01624.x"},{"key":"ref28","article-title":"Data Shapley: Equitable valuation of data for machine learning","author":"ghorbani","year":"2019"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1515\/9781400881970-018"},{"key":"ref29","article-title":"Is Shapley value fair? Improving client selection for mavericks in federated learning","author":"huang","year":"2021"}],"container-title":["IEEE Transactions on Big Data"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6687317\/10236926\/10138056.pdf?arnumber=10138056","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,25]],"date-time":"2023-09-25T18:35:58Z","timestamp":1695666958000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10138056\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10]]},"references-count":57,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.1109\/tbdata.2023.3280406","relation":{},"ISSN":["2332-7790","2372-2096"],"issn-type":[{"value":"2332-7790","type":"electronic"},{"value":"2372-2096","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10]]}}}