  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 hybrid setting, we
present an efficient reformulation of the Kernel Regularized Least Squares
algorithm, introduce two variants and validate them using well-established
datasets. Lastly, we discuss security measures to defend against possible
attacks.
