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Federated learning and Indigenous genomic data sovereignty

Indigenous peoples are under-represented in genomic datasets, which can lead to limited accuracy and utility of machine learning models in precision health. While open data sharing undermines rights of Indigenous communities to govern data decisions, federated learning may facilitate secure and community-consented data sharing.

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N.B. thanks J. T. Topham and G. Pistilli for their advice and feedback on the manuscript. K.S.T. and K.F. are grateful to the ENRICH (Equity for Indigenous Research and Innovation Coordinating Hub) program at New York University and University of Waikato, funded in part through a gift from the Minderoo Foundation, for their support for machine-focused solutions that uplift Indigenous data sovereignties.

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Correspondence to Nima Boscarino.

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Nature Machine Intelligence thanks Jantina de Vries and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Boscarino, N., Cartwright, R.A., Fox, K. et al. Federated learning and Indigenous genomic data sovereignty. Nat Mach Intell 4, 909–911 (2022).

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