{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T18:10:17Z","timestamp":1775326217879,"version":"3.50.1"},"reference-count":30,"publisher":"IEEE","license":[{"start":{"date-parts":[[2024,1,6]],"date-time":"2024-01-06T00:00:00Z","timestamp":1704499200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,1,6]],"date-time":"2024-01-06T00:00:00Z","timestamp":1704499200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,1,6]]},"DOI":"10.1109\/ccnc51664.2024.10454875","type":"proceedings-article","created":{"date-parts":[[2024,3,18]],"date-time":"2024-03-18T14:53:49Z","timestamp":1710773629000},"page":"943-949","source":"Crossref","is-referenced-by-count":3,"title":["On the Analysis of Model Poisoning Attacks Against Blockchain-Based Federated Learning"],"prefix":"10.1109","author":[{"given":"Rukayat","family":"Olapojoye","sequence":"first","affiliation":[{"name":"Texas Tech University,Department of Computer Science,Texas,USA"}]},{"given":"Mohamed","family":"Baza","sequence":"additional","affiliation":[{"name":"College of Charleston,Department of Computer Science,South Carolina,USA"}]},{"given":"Tara","family":"Salman","sequence":"additional","affiliation":[{"name":"Texas Tech University,Department of Computer Science,Texas,USA"}]}],"member":"263","reference":[{"key":"ref1","article-title":"Survey on federated learning threats: concepts, taxonomy on attacks and defenses, experimental study and challenges","author":"Rodr\u00edguez-Barroso","year":"2022","journal-title":"arXiv preprint"},{"key":"ref2","article-title":"Hybrid local sgd for federated learning with heterogeneous communications","volume-title":"International Conference on Learning Representations","author":"Guo","year":"2021"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP40776.2020.9054676"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2023.100547"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/3560816"},{"key":"ref6","first-page":"840","article-title":"Sageflow: Robust federated learning against both stragglers and adversaries","volume":"34","author":"Park","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref7","article-title":"Machine learning with adversaries: Byzantine tolerant gradient descent","volume":"30","author":"Blanchard","year":"2017","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/3560816"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/BCCA50787.2020.9274451"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2022.3213345"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3072611"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2018.2863956"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3068178"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/LCOMM.2019.2921755"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/3570953"},{"key":"ref16","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Artificial intelligence and statistics","author":"McMahan","year":"2017"},{"key":"ref17","volume-title":"The mnist database of handwritten digits","author":"LeCun","year":"1998"},{"key":"ref18","volume-title":"Flower documentation","year":"2021"},{"key":"ref19","volume-title":"Keras","author":"Chollet","year":"2015"},{"key":"ref20","volume-title":"Ethereum: A secure decentralized generalized transaction ledger","author":"Wood","year":"2015"},{"key":"ref21","volume-title":"Ganache","year":"2021"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2022.3170348"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2021.3138848"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CCNC49033.2022.9700513"},{"key":"ref25","first-page":"2938","article-title":"How to backdoor federated learning","volume-title":"International Conference on Artificial Intelligence and Statistics","author":"Bagdasaryan","year":"2020"},{"key":"ref26","first-page":"634","article-title":"Analyzing federated learning through an adversarial lens","volume-title":"International Conference on Machine Learning","author":"Bhagoji","year":"2019"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-63076-8_1"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2022.3169918"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539231"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TSC.2023.3294063"}],"event":{"name":"2024 IEEE 21st Consumer Communications & Networking Conference (CCNC)","location":"Las Vegas, NV, USA","start":{"date-parts":[[2024,1,6]]},"end":{"date-parts":[[2024,1,9]]}},"container-title":["2024 IEEE 21st Consumer Communications &amp; Networking Conference (CCNC)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10454139\/10454627\/10454875.pdf?arnumber=10454875","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,26]],"date-time":"2024-03-26T09:13:28Z","timestamp":1711444408000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10454875\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,6]]},"references-count":30,"URL":"https:\/\/doi.org\/10.1109\/ccnc51664.2024.10454875","relation":{},"subject":[],"published":{"date-parts":[[2024,1,6]]}}}