{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T13:06:41Z","timestamp":1753880801571,"version":"3.41.2"},"reference-count":0,"publisher":"World Scientific Pub Co Pte Ltd","issue":"06","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Artif. Intell. Tools"],"published-print":{"date-parts":[[2022,9]]},"abstract":"<jats:p> Distributed optimization algorithms are widely used for solving science and engineering problems in recent years. However, these algorithms become vulnerable in the face of external attacks, so it is urgent to develop novel resilient distributed optimization algorithms against attacks. In this paper, we design a novel median-based resilient distributed optimization algorithm to defend the Byzantine attack. The existence of transition matrix is proved, and the convergence and optimality of the proposed algorithm are analyzed. Simulation experiments are performed to verify the effectiveness of the proposed algorithm and the results show that it is more effective than the compared one and can get the best possible results against the attack. <\/jats:p>","DOI":"10.1142\/s0218213022400206","type":"journal-article","created":{"date-parts":[[2022,9,23]],"date-time":"2022-09-23T07:08:25Z","timestamp":1663916905000},"source":"Crossref","is-referenced-by-count":1,"title":["A Median-based Resilient Distributed Optimization Algorithm Against Byzantine Attack"],"prefix":"10.1142","volume":"31","author":[{"given":"Chentao","family":"Xu","sequence":"first","affiliation":[{"name":"School of Cyber Science and Engineering, Southeast University, Nanjing 210096, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7672-5129","authenticated-orcid":false,"given":"Qingshan","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Mathematics, Frontiers Science Center for Mobile Information Communication and Security, Southeast University, Nanjing 210096, China"},{"name":"Purple Mountain Laboratories, Nanjing 211111, China"}]}],"member":"219","published-online":{"date-parts":[[2022,9,22]]},"container-title":["International Journal on Artificial Intelligence Tools"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0218213022400206","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,23]],"date-time":"2022-09-23T07:08:27Z","timestamp":1663916907000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/10.1142\/S0218213022400206"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9]]},"references-count":0,"journal-issue":{"issue":"06","published-print":{"date-parts":[[2022,9]]}},"alternative-id":["10.1142\/S0218213022400206"],"URL":"https:\/\/doi.org\/10.1142\/s0218213022400206","relation":{},"ISSN":["0218-2130","1793-6349"],"issn-type":[{"type":"print","value":"0218-2130"},{"type":"electronic","value":"1793-6349"}],"subject":[],"published":{"date-parts":[[2022,9]]},"article-number":"2240020"}}