  Building machine learning models from decentralized datasets located in
different centers with federated learning (FL) is a promising approach to
circumvent local data scarcity while preserving privacy. However, the prominent
Cox proportional hazards (PH) model, used for survival analysis, does not fit
the FL framework, as its loss function is non-separable with respect to the
samples. The na\"ive method to bypass this non-separability consists in
calculating the losses per center, and minimizing their sum as an approximation
of the true loss. We show that the resulting model may suffer from important
performance loss in some adverse settings. Instead, we leverage the
discrete-time extension of the Cox PH model to formulate survival analysis as a
classification problem with a separable loss function. Using this approach, we
train survival models using standard FL techniques on synthetic data, as well
as real-world datasets from The Cancer Genome Atlas (TCGA), showing similar
performance to a Cox PH model trained on aggregated data. Compared to previous
works, the proposed method is more communication-efficient, more generic, and
more amenable to using privacy-preserving techniques.
