# FedPseudo: Pseudo value-based Deep Learning Models for Federated Survival Analysis

arXiv: https://arxiv.org/abs/2207.05247 · HTML: https://mlsanity.com/paper/2207.05247/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 20 references that are in the map.
- First posted on arXiv 12 Jul 2022.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Learning on Non-IID Data Silos: An Experimental Study](https://mlsanity.com/paper/2102.02079/) (2021)
- [DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network](https://mlsanity.com/paper/1606.00931/) (2016)
- [Time-to-Event Prediction with Neural Networks and Cox Regression](https://mlsanity.com/paper/1907.00825/) (2019)
- [Federated Survival Analysis with Discrete-Time Cox Models](https://mlsanity.com/paper/2006.08997/) (2020)
- [DNNSurv: Deep Neural Networks for Survival Analysis Using Pseudo Values](https://mlsanity.com/paper/1908.02337/) (2019)
- [Practical Challenges in Differentially-Private Federated Survival Analysis of Medical Data](https://mlsanity.com/paper/2202.03758/) (2022)

## Built on by (most cited first, 7 of 7)

- [Federated Survival Forests](https://mlsanity.com/paper/2302.02807/) (2023)
- [Scaling Survival Analysis in Healthcare with Federated Survival Forests: A Comparative Study on Heart Failure and Breast Cancer Genomics](https://mlsanity.com/paper/2308.02382/) (2023)
- [Heterogeneous Datasets for Federated Survival Analysis Simulation](https://mlsanity.com/paper/2301.12166/) (2023)
- [Addressing Data Heterogeneity in Federated Learning of Cox Proportional Hazards Models](https://mlsanity.com/paper/2407.14960/) (2024)
- [Bayesian Federated Inference for Survival Models](https://mlsanity.com/paper/2404.17464/) (2024)
- [Predicting Survival of Hemodialysis Patients using Federated Learning](https://mlsanity.com/paper/2412.10919/) (2024)
- [Federated Survival Analysis in Healthcare: A Multi-Model Evaluation on Cross-Institutional Heterogeneous Breast Cancer Data](https://mlsanity.com/paper/2606.23871/) (2026)

## Abstract (arXiv)

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.