Federated singular value decomposition for high dimensional data. (arXiv:2205.12109v1 [cs.LG])

Federated learning (FL) is emerging as a privacy-aware alternative to
classical cloud-based machine learning. In FL, the sensitive data remains in
data silos and only aggregated parameters are exchanged. Hospitals and research
institutions which are not willing to share their data can join a federated
study without breaching confidentiality. In addition to the extreme sensitivity
of biomedical data, the high dimensionality poses a challenge in the context of
federated genome-wide association studies (GWAS). In this article, we present a
federated …

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