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This work was supported in part by the National Natural Science Fund for Distinguished Young Scholars (62025205), in part by the National Natural Science Foundation of China under Grant 62541327, Grant 62532009, Grant U25B2042, and Grant 62302396, in part by the Natural Science Foundation of Shaanxi Province (Grant No.2024JC-YBQN-0665). (Corresponding author: Yao Zhang) Jing Zhang and Ke Huang are with the School of Computer Science and Technology, Xi’an University of Science and Technology, Xi’an 710600, China. Yao Zhang and Bin Guo are with the School of Computer Science, Northwestern Polytechnical University, Xi’an 710021, China. Z. Yu is with Harbin Engineering University, Harbin, Heilongjiang, China, and also with the School of Computer Science, Northwestern Polytechnical University, Xi’an, Shaanxi, China
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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.