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This work was supported in part by the National Natural Science Foundation of China under Grant No. U2268204 and 62172061; National Key R&D Program of China under Grant No. 2023YFB3308300; the Science and Technology Project of Sichuan Province under Grant No. 2024ZDZX0012, 2023ZHCG0011, 2021YFG0152. Additionally, this work was supported in part by China Scholarship Council (CSC) under Grant 202506240164.(Corresponding authors: Bing Guo)Mingjie Zhao, Cheng Dai, Bing Guo, Fei Chen, Kui Ye, and Xin Chen are with the College of Computer Scicnce, Sichuan University, Chengdu 610065, China (e-mail: zhaomingjie@stu.scu.edu.cn; daicheng@scu.edu.cn; guobing@scu.edu.cn; chenfei@stu.scu.edu.cn; 2021323040029@stu.scu.edu.cn; 2024323045015@stu.scu.edu.cn;). Mingjie Zhao is also a visiting student at Muroran Institute of Technology (e-mail:25061115p@muroran-it.ac.jp).Kaoru Ota is with the Graduate School of Information Sciences, To-hoku University, Sendai 980-8579, Japan.(e-mail: k.ota@tohoku.ac.jp)Mianxiong Dong is with the Department of Sciences and Informatics, Muroran Institute of Technology, Muroran 0508585, Japan. (e-mail: mxdong@muroran-it.ac.jp)
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A Shapley-incentivized blockchain boosts federated learning accuracy by 14% and thwarts 90% of malicious attacks in high-speed rail data sharing.