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Hongjuan Li, Hui Kang, and Jiahui Li are with the College of Computer Science and Technology, Jilin University, Changchun 130012, China, and also with the Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China (E-mails: hongjuan23@mails.jlu.edu.cn; kanghui@jlu.edu.cn; lijiahui@jlu.edu.cn). Geng Sun is with the College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China, and also with the College of Computing and Data Science, Nanyang Technological University, Singapore 639798 (E-mail: sungeng@jlu.edu.cn). Ruichen Zhang, Jiacheng Wang, and Dusit Niyato are with the College of Computing and Data Science, Nanyang Technological University, Singapore 639798 (E-mails:ruichen.zhang@ntu.edu.sg; jiacheng.wang@ntu.edu.sg; dniyato@ntu.edu.sg). Wei Ni is the School of Engineering, Edith Cowan University, Perth, WA 6027, and the School of Electrical and Data Engineering, the University of Technology Sydney, Sydney, NSW 2007, Australia (E-mail:wei.ni@ieee.org). Abbas Jamalipour is with the School of Electrical and Computer Engineering, The University of Sydney, Sydney, NSW 2006, Australia, and with the Graduate School of Information Sciences, Tohoku University, Sendai 980-8578, Japan (E-mail: a.jamalipour@ieee.org). (Corresponding authors: Jiahui Li and Geng Sun.)
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Federated learning can supercharge agentic AI in wireless networks by enabling collaborative local learning and parameter sharing, overcoming limitations of centralized architectures.
