{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T20:57:32Z","timestamp":1775509052749,"version":"3.50.1"},"reference-count":85,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Netw. Sci. Eng."],"published-print":{"date-parts":[[2026]]},"DOI":"10.1109\/tnse.2026.3674950","type":"journal-article","created":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T20:24:56Z","timestamp":1773779096000},"page":"7737-7754","source":"Crossref","is-referenced-by-count":0,"title":["FedSSV: Client Contribution for Secure Federated Learning Aggregation Using Scaled Shapley Values"],"prefix":"10.1109","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-3118-5375","authenticated-orcid":false,"given":"Samin Dehbashi","family":"Sani","sequence":"first","affiliation":[{"name":"Department of Computer Science, Texas Tech University, Lubbock, TX, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6332-8615","authenticated-orcid":false,"given":"Rukayat","family":"Olapojoye","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Texas Tech University, Lubbock, TX, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0022-5114","authenticated-orcid":false,"given":"Tara","family":"Salman","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Texas Tech University, Lubbock, TX, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9083-5452","authenticated-orcid":false,"given":"Tianxi","family":"Ji","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Texas Tech University, Lubbock, TX, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1466-3596","authenticated-orcid":false,"given":"Marcio Andrey","family":"Teixeira","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Texas Tech University, Lubbock, TX, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.31449\/inf.v49i17.7707"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.1657"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/OJCOMS.2024.3506214"},{"key":"ref4","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. Artif. Intell. Statist.","author":"McMahan","year":"2017"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3011726"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-020-00323-1"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-021-00489-2"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.01.1900525"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.001.1900461"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671899"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.125493"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2023.02.021"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.3389\/fdgth.2024.1261031"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110658"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-023-10563-8"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2023.127225"},{"key":"ref17","first-page":"634","article-title":"Analyzing federated learning through an adversarial lens","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Bhagoji","year":"2019"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.2975749"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-81732-0"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599500"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.cor.2008.04.004"},{"key":"ref22","article-title":"Fashion-MNIST: A novel image dataset for benchmarking machine learning algorithms","author":"Xiao"},{"key":"ref23","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2018.161"},{"key":"ref25","first-page":"118","article-title":"Machine learning with adversaries: Byzantine tolerant gradient descent","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Blanchard","year":"2017"},{"key":"ref26","first-page":"5650","article-title":"Byzantine-robust distributed learning: Towards optimal statistical rates","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Yin","year":"2018"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3329061"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.32604\/cmc.2024.058926"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.001.2000200"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3151193"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-78239-z"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/3501296"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/s40012-023-00382-1"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-06574-w"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2020.2988604"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-85082-1_21"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107330"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-77196-x"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-60548-3_18"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-73334-7"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1016\/j.seta.2022.102987"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TIV.2023.3332675"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1007\/s10586-024-04629-7"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCS60910.2024.00139"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2025.127409"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1145\/3605098.3635896"},{"key":"ref47","first-page":"1605","article-title":"Local model poisoning attacks to Byzantine-Robust federated learning","volume-title":"Proc. 29th USENIX Secur. Symp.","author":"Fang","year":"2020"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/SP46214.2022.9833647"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1145\/3551636"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/CCNC51664.2024.10454875"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.23919\/EUSIPCO63237.2025.11226343"},{"key":"ref52","article-title":"Generalized byzantine-tolerant SGD","author":"Xie"},{"key":"ref53","first-page":"3521","article-title":"The hidden vulnerability of distributed learning in byzantium","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Guerraoui","year":"2018"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1145\/3724113"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2026.104155"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.14778\/3659437.3659459"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1145\/267460.267491"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN52387.2021.9534451"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1515\/9781400881970-018"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1145\/3501811"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.14778\/3587136.3587141"},{"key":"ref62","first-page":"16104","article-title":"Gradient driven rewards to guarantee fairness in collaborative machine learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Xu","year":"2021"},{"key":"ref63","first-page":"34574","article-title":"CS-Shapley: Class-wise Shapley values for data valuation in classification","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"35","author":"Schoch","year":"2022"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-63076-8_11"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1145\/3588728"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.52202\/068431-0948"},{"key":"ref67","article-title":"Improving Kernelshap: Practical Shapley value estimation via linear regression","author":"Covert"},{"key":"ref68","first-page":"2242","article-title":"Data Shapley: Equitable valuation of data for machine learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Ghorbani","year":"2019"},{"key":"ref69","first-page":"2938","article-title":"How to backdoor federated learning","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Bagdasaryan","year":"2020"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/SPW59333.2023.00011"},{"key":"ref71","first-page":"6357","article-title":"Ditto: Fair and robust federated learning through personalization","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Li","year":"2021"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2021.24498"},{"key":"ref73","article-title":"On the inflation of KNN-shapley value","author":"Yang"},{"key":"ref74","volume-title":"Interpretable Mach. Learn.","author":"Molnar","year":"2020"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/CDC.2012.6426691"},{"key":"ref76","article-title":"Local SGD converges fast and communicates little","author":"Stich"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33015693"},{"key":"ref78","article-title":"Cooperative SGD: A unified framework for the design and analysis of communication-efficient SGD algorithms","author":"Wang"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1145\/3579856.3582836"},{"key":"ref80","first-page":"1","article-title":"On the convergence of FedAvg on non-IID data","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Li","year":"2020"},{"key":"ref81","first-page":"5132","article-title":"SCAFFOLD: Stochastic controlled averaging for federated learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Karimireddy","year":"2020"},{"issue":"1","key":"ref82","first-page":"1837","article-title":"Distributions of angles in random packing on spheres","volume":"14","author":"Cai","year":"2013","journal-title":"J. Mach. Learn. Res."},{"key":"ref83","first-page":"1","article-title":"PyTorch: An imperative style, high-performance deep learning library","volume-title":"Proc. Adv. Neural Inform. Process. Syst.","volume":"32","author":"Paszke","year":"2019"},{"key":"ref84","first-page":"8635","article-title":"A little is enough: Circumventing defenses for distributed learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Baruch","year":"2019"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.52202\/068431-2537"}],"container-title":["IEEE Transactions on Network Science and Engineering"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6488902\/11264281\/11435920.pdf?arnumber=11435920","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T19:58:14Z","timestamp":1775505494000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11435920\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":85,"URL":"https:\/\/doi.org\/10.1109\/tnse.2026.3674950","relation":{},"ISSN":["2327-4697","2334-329X"],"issn-type":[{"value":"2327-4697","type":"electronic"},{"value":"2334-329X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]}}}