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Qinqi Lin is with the School of Science and Engineering, Shenzhen Institute of Artificial Intelligence and Robotics for Society, The Chinese University of Hong Kong, Shenzhen, China (e-mail: qinqilin@link.cuhk.edu.cn). Ningning Ding is with the Data Science and Analytics Thrust, Information Hub, Hong Kong University of Science and Technology (Guangzhou), China (e-mail: ningningding@hkust-gz.edu.cn). Lingjie Duan is with the Internet of Things Thrust, Information Hub, Hong Kong University of Science and Technology (Guangzhou), China (e-mail: lingjieduan@hkust-gz.edu.cn). Jianwei Huang is with the School of Science and Engineering, Shenzhen Institute of Artificial Intelligence and Robotics for Society, The Chinese University of Hong Kong, Shenzhen, China (corresponding author
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Ignoring privacy differences between clients in federated learning can hurt accuracy by 10%, but a new privacy-aware client selection method fixes this.
