# Federated Learning for Predicting Mild Cognitive Impairment to Dementia Conversion

arXiv: https://arxiv.org/abs/2503.03489 · HTML: https://mlsanity.com/paper/2503.03489/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 35 references that are in the map.
- First posted on arXiv 5 Mar 2025.

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

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Hacking Smart Machines with Smarter Ones: How to Extract Meaningful Data from Machine Learning Classifiers](https://mlsanity.com/paper/1306.4447/) (2013)
- [Secure Neuroimaging Analysis using Federated Learning with Homomorphic Encryption](https://mlsanity.com/paper/2108.03437/) (2021)
- [SecureFedYJ: a safe feature Gaussianization protocol for Federated Learning](https://mlsanity.com/paper/2210.01639/) (2022)
- [Overcoming the Challenges of Batch Normalization in Federated Learning](https://mlsanity.com/paper/2405.14670/) (2024)
- [Analysis of Total Variation Minimization for Clustered Federated Learning](https://mlsanity.com/paper/2403.06298/) (2024)

## Abstract (arXiv)

Dementia is a progressive condition that impairs an individual's cognitive health and daily functioning, with mild cognitive impairment (MCI) often serving as its precursor. The prediction of MCI to dementia conversion has been well studied, but previous studies have almost always focused on traditional Machine Learning (ML) based methods that require sharing sensitive clinical information to train predictive models. This study proposes a privacy-enhancing solution using Federated Learning (FL) to train predictive models for MCI to dementia conversion without sharing sensitive data, leveraging socio demographic and cognitive measures. We simulated and compared two network architectures, Peer to Peer (P2P) and client-server, to enable collaborative learning. Our results demonstrated that FL had comparable predictive performance to centralized ML, and each clinical site showed similar performance without sharing local data. Moreover, the predictive performance of FL models was superior to site specific models trained without collaboration. This work highlights that FL can eliminate the need for data sharing without compromising model efficacy.