  Survival analysis, time-to-event analysis, is an important problem in
healthcare since it has a wide-ranging impact on patients and palliative care.
Many survival analysis methods have assumed that the survival data is centrally
available either from one medical center or by data sharing from multi-centers.
However, the sensitivity of the patient attributes and the strict privacy laws
have increasingly forbidden sharing of healthcare data. To address this
challenge, the research community has looked at the solution of decentralized
training and sharing of model parameters using the Federated Learning (FL)
paradigm. In this paper, we study the utilization of FL for performing survival
analysis on distributed healthcare datasets. Recently, the popular Cox
proportional hazard (CPH) models have been adapted for FL settings; however,
due to its linearity and proportional hazards assumptions, CPH models result in
suboptimal performance, especially for non-linear, non-iid, and heavily
censored survival datasets. To overcome the challenges of existing federated
survival analysis methods, we leverage the predictive accuracy of the deep
learning models and the power of pseudo values to propose a first-of-its-kind,
pseudo value-based deep learning model for federated survival analysis (FSA)
called FedPseudo. Furthermore, we introduce a novel approach of deriving pseudo
values for survival probability in the FL settings that speeds up the
computation of pseudo values. Extensive experiments on synthetic and real-world
datasets show that our pseudo valued-based FL framework achieves similar
performance as the best centrally trained deep survival analysis model.
Moreover, our proposed FL approach obtains the best results for various
censoring settings.
