# Federated Survival Analysis with Discrete-Time Cox Models

arXiv: https://arxiv.org/abs/2006.08997 · HTML: https://mlsanity.com/paper/2006.08997/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2020.

## Built on by (most cited first, 19 of 19)

- [FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings](https://mlsanity.com/paper/2210.04620/) (2022)
- [From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcare](https://mlsanity.com/paper/2409.09727/) (2024)
- [Multimodal Federated Learning in Healthcare: a Review](https://mlsanity.com/paper/2310.09650/) (2023)
- [Federated Survival Forests](https://mlsanity.com/paper/2302.02807/) (2023)
- [FedPseudo: Pseudo value-based Deep Learning Models for Federated Survival Analysis](https://mlsanity.com/paper/2207.05247/) (2022)
- [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)
- [A Federated Cox Model with Non-Proportional Hazards](https://mlsanity.com/paper/2207.05050/) (2022)
- [Addressing Data Heterogeneity in Federated Learning of Cox Proportional Hazards Models](https://mlsanity.com/paper/2407.14960/) (2024)
- [SRATTA : Sample Re-ATTribution Attack of Secure Aggregation in Federated Learning](https://mlsanity.com/paper/2306.07644/) (2023)
- [FedECA: Federated External Control Arms for Causal Inference with Time-To-Event Data in Distributed Settings](https://mlsanity.com/paper/2311.16984/) (2023)
- [FSL-BDP: Federated Survival Learning with Bayesian Differential Privacy for Credit Risk Modeling](https://mlsanity.com/paper/2601.11134/) (2026)
- [A Comprehensive View of Personalized Federated Learning on Heterogeneous Clinical Datasets](https://mlsanity.com/paper/2309.16825/) (2023)
- [Enhancing Federated Survival Analysis through Peer-Driven Client Reputation in Healthcare](https://mlsanity.com/paper/2505.16190/) (2025)
- [Predicting Survival of Hemodialysis Patients using Federated Learning](https://mlsanity.com/paper/2412.10919/) (2024)
- [A Simulated Federated Analysis of MS-Induced Brain Lesions](https://mlsanity.com/paper/2605.08223/) (2026)
- [Federated Survival Analysis in Healthcare: A Multi-Model Evaluation on Cross-Institutional Heterogeneous Breast Cancer Data](https://mlsanity.com/paper/2606.23871/) (2026)
- [Collaborative System Failure Prognostics via Federated Longitudinal-Survival Modeling](https://mlsanity.com/paper/2607.26038/) (2026)
- [Distributionally Robust Survival Models under Subpopulation Shift and Outlier Contamination](https://mlsanity.com/paper/2610.02868/) (2026)

## Abstract (arXiv)

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. Instead, we leverage the discrete-time extension of the Cox PH model to formulate survival analysis as a classification problem with a separable loss function. Using this approach, we train survival models using standard FL techniques on synthetic data, as well as real-world datasets from The Cancer Genome Atlas (TCGA), showing similar performance to a Cox PH model trained on aggregated data. Compared to previous works, the proposed method is more communication-efficient, more generic, and more amenable to using privacy-preserving techniques.