{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:08:47Z","timestamp":1775066927267,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,5,11]],"date-time":"2021-05-11T00:00:00Z","timestamp":1620691200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The fast proliferation of edge computing devices brings an increasing growth of data, which directly promotes machine learning (ML) technology development. However, privacy issues during data collection for ML tasks raise extensive concerns. To solve this issue, synchronous federated learning (FL) is proposed, which enables the central servers and end devices to maintain the same ML models by only exchanging model parameters. However, the diversity of computing power and data sizes leads to a significant difference in local training data consumption, and thereby causes the inefficiency of FL. Besides, the centralized processing of FL is vulnerable to single-point failure and poisoning attacks. Motivated by this, we propose an innovative method, federated learning with asynchronous convergence (FedAC) considering a staleness coefficient, while using a blockchain network instead of the classic central server to aggregate the global model. It avoids real-world issues such as interruption by abnormal local device training failure, dedicated attacks, etc. By comparing with the baseline models, we implement the proposed method on a real-world dataset, MNIST, and achieve accuracy rates of 98.96% and 95.84% in both horizontal and vertical FL modes, respectively. Extensive evaluation results show that FedAC outperforms most existing models.<\/jats:p>","DOI":"10.3390\/s21103335","type":"journal-article","created":{"date-parts":[[2021,5,11]],"date-time":"2021-05-11T22:53:40Z","timestamp":1620773620000},"page":"3335","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":60,"title":["Blockchain-Enabled Asynchronous Federated Learning in Edge Computing"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9803-9776","authenticated-orcid":false,"given":"Yinghui","family":"Liu","sequence":"first","affiliation":[{"name":"School of Information Technology, Deakin University, Burwood, VIC 3125, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2944-4647","authenticated-orcid":false,"given":"Youyang","family":"Qu","sequence":"additional","affiliation":[{"name":"Deakin Blockchain Innovation Lab, School of Information Technology, Deakin University, Burwood, VIC 3125, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0819-7269","authenticated-orcid":false,"given":"Chenhao","family":"Xu","sequence":"additional","affiliation":[{"name":"Deakin Blockchain Innovation Lab, School of Information Technology, Deakin University, Burwood, VIC 3125, Australia"}]},{"given":"Zhicheng","family":"Hao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Smart Tourism, Beijing Union University, Beijing 100101, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3008-6285","authenticated-orcid":false,"given":"Bruce","family":"Gu","sequence":"additional","affiliation":[{"name":"College of Engineering and Science, Victoria University, Footscray, VIC 3011, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1126\/science.aaa8415","article-title":"Machine learning: Trends, perspectives, and prospects","volume":"349","author":"Jordan","year":"2015","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"94570B","DOI":"10.1117\/12.2181526","article-title":"Deep learning and face recognition: The state of the art","volume":"Volume 9457","author":"Balaban","year":"2015","journal-title":"Biometric and Surveillance Technology for Human and Activity Identification XII"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.ijmedinf.2018.01.007","article-title":"Federated learning of predictive models from federated electronic health records","volume":"112","author":"Brisimi","year":"2018","journal-title":"Int. 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