# Federated Learning for Multi-Center Sepsis Early Prediction with Privacy-Preserving

arXiv: https://arxiv.org/abs/2606.04338 · HTML: https://mlsanity.com/paper/2606.04338/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 70 references that are in the map.
- First posted on arXiv 3 Jun 2026.

## Builds on (most cited first, 4 of 4)

- [NCSAC: Effective Neural Community Search via Attribute-augmented Conductance](https://mlsanity.com/paper/2511.04712/) (2025)
- [Mini-Hes: A Parallelizable Second-order Latent Factor Analysis Model](https://mlsanity.com/paper/2402.11948/) (2024)
- [End-to-end Graph Learning Approach for Cognitive Diagnosis of Student Tutorial](https://mlsanity.com/paper/2411.00845/) (2024)
- [Concept Factorization via Self-Representation and Adaptive Graph Structure Learning](https://mlsanity.com/paper/2505.03390/) (2025)

## Abstract (arXiv)

Privacy-sensitive and distributed characteristics of multi-center medical data bring severe obstacles to centralized modeling for accurate early prediction of sepsis. Federated learning (FL) has attracted growing attention as a promising framework for collaborative model development, as it allows multiple institutions to jointly train predictive models without directly sharing or centralizing raw data. Nevertheless, its practical performance, robustness, and privacy-preserving benefits remain insufficiently evaluated using real-world clinical datasets. To bridge this gap, this study systematically examines the application of federated learning to multi-center sepsis prediction. The experimental dataset consists of 648 clinically screened samples collected from three tertiary hospitals in China, with rigorous inclusion and exclusion criteria. We establish a centralized training paradigm as the performance baseline, and then implement a horizontal federated learning framework for distributed collaborative modeling. Extensive experimental results demonstrate that the federated learning-based model achieves highly comparable prediction accuracy to the centralized counterpart, while fundamentally avoiding privacy leakage. Further privacy security analysis verifies that malicious attackers cannot reconstruct the original patient data from the transmitted model parameters, indicating strong resistance against data reconstruction attacks. This work not only validates the practicality and security of federated learning in clinical sepsis prediction, but also provides a reliable and feasible solution for privacy-preserving multi-center medical collaboration.