A hybrid federated kernel regularized least squares algorithm

Federated learning is becoming an increasingly viable and accepted strategy for building machine learning models in critical privacy-preserving scenarios such as clinical settings. Often, the data involved is not limited to clinical data but also includes additional omics features (e.g. proteomics). Consequently, data is distributed not only across hospitals but also across omics centers, which are labs capable of generating such additional features from biosamples. This scenario leads to a hybrid setting where data is scattered both in terms of samples and features. In this setting, we present a novel efficient federated reformulation of the Kernel Regularized Least Squares algorithm which leverages a randomized version of the Nyström method, introduce two variants for the optimization process and validate them using well-established datasets. In principle, the presented core ideas could be applied to any other kernel method to make it federated. Lastly, we discuss security measures to defend against possible attacks.