# Personalized and privacy-preserving federated heterogeneous medical image analysis with PPPML-HMI

## TL;DR

This paper introduces PPPML-HMI, an open-source federated learning framework that achieves personalized and privacy-preserving medical image analysis across heterogeneous data sources without modifying models or sharing private data.

## Contribution

It is the first to combine personalization and privacy protection simultaneously in federated medical imaging analysis using novel cyclic secure aggregation and homomorphic encryption.

## Key findings

- Achieved ~5% higher Dice score in COVID-19 CT segmentation.
- Demonstrated robustness across different tasks, networks, users, and sample sizes.
- Validated strong privacy-preserving capabilities against gradient leakage attacks.

## Abstract

Heterogeneous data is endemic due to the use of diverse models and settings of devices by hospitals in the field of medical imaging. However, there are few open-source frameworks for federated heterogeneous medical image analysis with personalization and privacy protection simultaneously without the demand to modify the existing model structures or to share any private data. In this paper, we proposed PPPML-HMI, an open-source learning paradigm for personalized and privacy-preserving federated heterogeneous medical image analysis. To our best knowledge, personalization and privacy protection were achieved simultaneously for the first time under the federated scenario by integrating the PerFedAvg algorithm and designing our novel cyclic secure aggregation with the homomorphic encryption algorithm. To show the utility of PPPML-HMI, we applied it to a simulated classification task namely the classification of healthy people and patients from the RAD-ChestCT Dataset, and one real-world segmentation task namely the segmentation of lung infections from COVID-19 CT scans. For the real-world task, PPPML-HMI achieved $\sim$5\% higher Dice score on average compared to conventional FL under the heterogeneous scenario. Meanwhile, we applied the improved deep leakage from gradients to simulate adversarial attacks and showed the solid privacy-preserving capability of PPPML-HMI. By applying PPPML-HMI to both tasks with different neural networks, a varied number of users, and sample sizes, we further demonstrated the strong robustness of PPPML-HMI.