Recent research has shown the potential for neural networks
to improve upon classical survival models such as the Cox model, which
is widely used in clinical practice. Neural networks, however, typically
rely on data that are centrally available, whereas healthcare data are
frequently held in secure silos. We present a federated Cox model that
accommodates this data setting and also relaxes the proportional hazards
assumption, allowing time-varying covariate effects. In this latter respect,
our model does not require explicit specification of the time-varying ef-
fects, reducing upfront organisational costs compared to previous works.
We experiment with publicly available clinical datasets and demonstrate
that the federated model is able to perform as well as a standard model.