# Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data

arXiv: https://arxiv.org/abs/1810.08553 · HTML: https://mlsanity.com/paper/1810.08553/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2018.

## Built on by (most cited first, 30 of 46)

- [FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space](https://mlsanity.com/paper/2103.06030/) (2021)
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- [Personalizing Federated Medical Image Segmentation via Local Calibration](https://mlsanity.com/paper/2207.04655/) (2022)
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- [Membership Inference Attacks on Deep Regression Models for Neuroimaging](https://mlsanity.com/paper/2105.02866/) (2021)
- [IOP-FL: Inside-Outside Personalization for Federated Medical Image Segmentation](https://mlsanity.com/paper/2204.08467/) (2022)
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- [FedGES: A Federated Learning Approach for BN Structure Learning](https://mlsanity.com/paper/2502.01538/) (2025)
- [Spatio-Temporal Representation Decoupling and Enhancement for Federated Instrument Segmentation in Surgical Videos](https://mlsanity.com/paper/2506.23759/) (2025)
- [A Comparative Study of Federated Learning Models for COVID-19 Detection](https://mlsanity.com/paper/2303.16141/) (2023)
- [Privacy-Preserving Edge Federated Learning for Intelligent Mobile-Health Systems](https://mlsanity.com/paper/2405.05611/) (2024)
- [FedMomentum: Preserving LoRA Training Momentum in Federated Fine-Tuning](https://mlsanity.com/paper/2603.08014/) (2026)
- [Aggregation Delayed Federated Learning](https://mlsanity.com/paper/2108.07433/) (2021)
- [Avoid Adversarial Adaption in Federated Learning by Multi-Metric Investigations](https://mlsanity.com/paper/2306.03600/) (2023)

## Abstract (arXiv)

At this moment, databanks worldwide contain brain images of previously unimaginable numbers. Combined with developments in data science, these massive data provide the potential to better understand the genetic underpinnings of brain diseases. However, different datasets, which are stored at different institutions, cannot always be shared directly due to privacy and legal concerns, thus limiting the full exploitation of big data in the study of brain disorders. Here we propose a federated learning framework for securely accessing and meta-analyzing any biomedical data without sharing individual information. We illustrate our framework by investigating brain structural relationships across diseases and clinical cohorts. The framework is first tested on synthetic data and then applied to multi-centric, multi-database studies including ADNI, PPMI, MIRIAD and UK Biobank, showing the potential of the approach for further applications in distributed analysis of multi-centric cohorts