Federated Survival Analysis with Discrete-Time Cox Models

Understand

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.