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      Federated Learning Meets Intelligence Reflection Surface in Drones for Enabling 6G Networks: Challenges and Opportunities

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          Diversity and multiplexing: a fundamental tradeoff in multiple-antenna channels

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            A study of the behavior of several methods for balancing machine learning training data

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              Federated Learning: Challenges, Methods, and Future Directions

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                Author and article information

                Contributors
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                Journal
                IEEE Access
                IEEE Access
                Institute of Electrical and Electronics Engineers (IEEE)
                2169-3536
                2023
                2023
                : 11
                : 130860-130887
                Affiliations
                [1 ]Department of Smart Technologies, Moscow Polytechnic University, Moscow, Russia
                [2 ]Electrical Engineering Department, Faculty of Engineering, Ibb University, Ibb, Yemen
                [3 ]School of Computer Science, Shenyang Aerospace University, Shenyang, China
                [4 ]Department of CSE, IIIT Naya Raipur, Raipur, Chhattisgarh, India
                [5 ]Department of Electronics and Communication Engineering, FET, MJP Rohilkhand University, Bareilly, Uttar Pradesh, India
                [6 ]Department of EC, Invertis University, Bareilly, Uttar Pradesh, India
                [7 ]Department of Electronics Engineering, Indian Institute of Technology (Banaras Hindu University) Varanasi, Varanasi, Uttar Pradesh, India
                Article
                10.1109/ACCESS.2023.3323399
                46aca986-985f-405f-8d1a-69ea61eb3742
                © 2023

                https://creativecommons.org/licenses/by-nc-nd/4.0/

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