# A Federated Cox Model with Non-Proportional Hazards

arXiv: https://arxiv.org/abs/2207.05050 · HTML: https://mlsanity.com/paper/2207.05050/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 29 references that are in the map.
- First posted on arXiv 11 Jul 2022.

## Builds on (most cited first, 15 of 15)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [The Future of Digital Health with Federated Learning](https://mlsanity.com/paper/2003.08119/) (2020)
- [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)
- [Machine Learning for Survival Analysis: A Survey](https://mlsanity.com/paper/1708.04649/) (2017)
- [A Scalable Discrete-Time Survival Model for Neural Networks](https://mlsanity.com/paper/1805.00917/) (2018)
- [Continuous and Discrete-Time Survival Prediction with Neural Networks](https://mlsanity.com/paper/1910.06724/) (2019)
- [Deep learning cardiac motion analysis for human survival prediction](https://mlsanity.com/paper/1810.03382/) (2018)
- [Federated Survival Analysis with Discrete-Time Cox Models](https://mlsanity.com/paper/2006.08997/) (2020)
- [Deep Learning for Patient-Specific Kidney Graft Survival Analysis](https://mlsanity.com/paper/1705.10245/) (2017)
- [Survival stacking: casting survival analysis as a classification problem](https://mlsanity.com/paper/2107.13480/) (2021)
- [Heterogeneous Data-Aware Federated Learning](https://mlsanity.com/paper/2011.06393/) (2020)
- [Deep Convolutional Neural Networks for Imaging Data Based Survival Analysis of Rectal Cancer](https://mlsanity.com/paper/1901.01449/) (2019)

## Built on by (most cited first, 4 of 4)

- [Recent Methodological Advances in Federated Learning for Healthcare](https://mlsanity.com/paper/2310.02874/) (2023)
- [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)
- [Collaborative System Failure Prognostics via Federated Longitudinal-Survival Modeling](https://mlsanity.com/paper/2607.26038/) (2026)

## Abstract (arXiv)

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 effects, 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.