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Artificial Intelligence in Health                            Federated learning health stack against pandemics



            Acknowledgments                                       people-margins-suffer-most [Last accessed on 2024 Dec 14].

            None.                                                 doi: 10.1126/science.abc7832
                                                               5.   Pathak  AD,  Saran  D,  Mishra  S,  Hitesh  M,  Bathula  S,
            Funding                                               Sahu KK. Smart war on COVID-19 and global pandemics:
            None.                                                 Integrated AI and blockchain ecosystem. In: Computational
                                                                  Modeling and Data Analysis in COVID-19 Research. United
            Conflict of interest                                  States: CRC Press; 2021. p. 67-94.

            The authors declare that they have no competing interests.      doi: 10.1201/9781003137481-5
                                                               6.   McMahan B, Moore E, Ramage D, Hampson S, Arcas BA.
            Author contributions                                  Communication-efficient learning of deep networks from
            Conceptualization: Kisor Kumar Sahu                   decentralized data. In:  Artificial  Intelligence  and  Statistics.
            Formal analysis: All authors                          Hamburg: Statista; 2017. p. 1273-1282.
            Investigation: Rojalini Tripathy                      doi: 10.48550/arXiv.1602.05629
            Methodology: All authors                           7.   Yurdem B, Kuzlu M, Gullu MK, Catak FO, Tabassum M.
            Visualization: All authors                            Federated learning: Overview, strategies, applications, tools
            Writing – original draft: All authors                 and future directions. Heliyon. 2024;10:e38137.
            Writing – review & editing: All authors               doi: 10.1016/j.heliyon.2024.e38137
            Ethics approval and consent to participate
                                                               8.   Nguyen DC, Pham QV, Pathirana PN,  et al. Federated
            Not applicable.                                       learning for smart healthcare: A survey. ACM Comput Surv.
                                                                  2022;55(3):1-37.
            Consent for publication
                                                                  doi: 10.1145/3501296
            Not applicable.                                    9.   Rieke N, Hancox J, Li W, et al. The future of digital health

            Availability of data                                  with federated learning. NPJ Digit Med. 2020;3(1):119.
                                                                  doi: 10.1038/s41746-020-00323-1
            Data are available at the following resource: Deng, L. The
            MNIST database of handwritten digit images for machine   10.  Sheller MJ, Edwards B, Reina GA, et al. Federated learning
            learning research. IEEE signal processing magazine;2012.   in medicine: Facilitating multi-institutional collaborations
            29(6), 141-142. doi: 10.1109/MSP.2012.2211477 and     without sharing patient data. Sci Rep. 2020;10(1):12598.
            code can be shared upon reasonable request over email:      doi: 10.1038/s41598-020-69250-1
            kisorsahu@iitbbs.ac.in.                            11.  Sun T, Li D, Wang B. Decentralized federated averaging.
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            Volume 2 Issue 4 (2025)                         88                          doi: 10.36922/AIH025080013
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