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Federated learning is not a cure-all for data ethics

Although federated learning is often seen as a promising solution to allow AI innovation while addressing privacy concerns, we argue that this technology does not fix all underlying data ethics concerns. Benefiting from federated learning in digital health requires acknowledgement of its limitations.

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  1. Brauneck, A. et al. Nat. Mach. Intell. 5, 2–4 (2023).

    Article  Google Scholar 

  2. Rieke, N. et al. NPJ Digit. Med. 3, 119 (2020).

    Article  Google Scholar 

  3. Kaissis, G. A. et al. Nat. Mach. Intell. 2, 305–311 (2020).

    Article  Google Scholar 

  4. Hitaj, B., Ateniese, G. & Perez-Cruz, F. In CCS’17: Proc. 2017 ACM SIGSAC Conference on Computer and Communications Security 603–618 (ACM, 2017).

  5. Geiping, J., Bauermeister, H., Dröge, H. & Moeller, M. In Proc. 33rd International Conference on Advances in Neural Information Processing Systems 16937–16947 (2020).

  6. Kairouz, P. et al. Mach. Learn. 14, 1–210 (2021).

    Google Scholar 

  7. Bagdasaryan, E. et al. In Proc. 23rd International Conference on Artificial Intelligence and Statistics 2938–2948 (PMLR, 2020).

  8. Crowson, M. G. et al. PLoS Digit. Health 1, e0000033 (2022).

    Article  Google Scholar 

  9. González-García, J. et al. Arch. Public Health 79, 221 (2021).

    Article  Google Scholar 

  10. Shi, Y., Yu, H. & Leung, C. In IEEE Transactions on Neural Network Learning Systems 1–17 (2023).

  11. Rossello, S., Muñoz-González, L. & Díaz Morales, R. Computerrecht: Tijdschrift voor Informatica, Telecommunicatie en Recht 3, 273–279 (2021).

    Google Scholar 

  12. Sandvik, K. B. Big Data Soc. 7, 2053951720939985 (2020).

    Article  Google Scholar 

  13. Sharon, T. Ethics Inf. Technol. 23, 45–57 (2021).

    Article  Google Scholar 

  14. Galtier, M. N. & Marini, C. Preprint at (2019).

  15. Martinez, I., Francis, S. & Hafid, A. S. In International Conference on Cyber-enabled Distributed Computing and Knowledge Discovery (CyberC) 2019 50–57 (IEEE, 2019).

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We thank M. Ibberson for his insightful thoughts and valuable feedback to the manuscript.

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Correspondence to Marieke Bak.

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Nature Machine Intelligence thanks Umit Topaloglu, Kristin Kostick-Quenet and Sascha Rank for their contribution to the peer review of this work.

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Bak, M., Madai, V.I., Celi, L.A. et al. Federated learning is not a cure-all for data ethics. Nat Mach Intell 6, 370–372 (2024).

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