# Federated Markov Imputation: Privacy-Preserving Temporal Imputation in Multi-Centric ICU Environments

arXiv: https://arxiv.org/abs/2509.20867 · HTML: https://mlsanity.com/paper/2509.20867/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 10 references that are in the map.
- First posted on arXiv 25 Sep 2025.

## Builds on (most cited first, 2 of 2)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Learning with Instance-Dependent Noisy Label](https://mlsanity.com/paper/2312.10324/) (2023)

## Abstract (arXiv)

Missing data is a persistent challenge in federated learning on electronic health records, particularly when institutions collect time-series data at varying temporal granularities. To address this, we propose Federated Markov Imputation (FMI), a privacy-preserving method that enables Intensive Care Units (ICUs) to collaboratively build global transition models for temporal imputation. We evaluate FMI on a real-world sepsis onset prediction task using the MIMIC-IV dataset and show that it outperforms local imputation baselines, especially in scenarios with irregular sampling intervals across ICUs.