# Privacy Risks Analysis and Mitigation in Federated Learning for Medical Images

arXiv: https://arxiv.org/abs/2311.06643 · HTML: https://mlsanity.com/paper/2311.06643/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 20 references that are in the map.
- First posted on arXiv 11 Nov 2023.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Deep Leakage from Gradients](https://mlsanity.com/paper/1906.08935/) (2019)
- [Inverting Gradients -- How easy is it to break privacy in federated learning?](https://mlsanity.com/paper/2003.14053/) (2020)
- [Federated Learning for Smart Healthcare: A Survey](https://mlsanity.com/paper/2111.08834/) (2021)
- [Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Results](https://mlsanity.com/paper/2001.05647/) (2020)
- [Gradient-Leakage Resilient Federated Learning](https://mlsanity.com/paper/2107.01154/) (2021)
- [Securing Distributed SGD against Gradient Leakage Threats](https://mlsanity.com/paper/2305.06473/) (2023)
- [Efficient Private Statistics with Succinct Sketches](https://mlsanity.com/paper/1508.06110/) (2015)
- [Analysing the Influence of Attack Configurations on the Reconstruction of Medical Images in Federated Learning](https://mlsanity.com/paper/2204.13808/) (2022)

## Built on by (most cited first, 7 of 7)

- [Security and Privacy Challenges of Large Language Models: A Survey](https://mlsanity.com/paper/2402.00888/) (2024)
- [Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challenges](https://mlsanity.com/paper/2407.06754/) (2024)
- [On the Efficiency of Privacy Attacks in Federated Learning](https://mlsanity.com/paper/2404.09430/) (2024)
- [In-depth Analysis of Privacy Threats in Federated Learning for Medical Data](https://mlsanity.com/paper/2409.18907/) (2024)
- [Automated Road Extraction from Satellite Imagery Integrating Dense Depthwise Dilated Separable Spatial Pyramid Pooling with DeepLabV3+](https://mlsanity.com/paper/2410.14836/) (2024)
- [A Diversity-optimized Deep Ensemble Approach for Accurate Plant Leaf Disease Detection](https://mlsanity.com/paper/2511.16982/) (2025)
- [STAR-FL: Secure Federated Learning with Spatial-Temporal Analysis and Robust Aggregation](https://mlsanity.com/paper/2608.14861/) (2026)

## Abstract (arXiv)

Federated learning (FL) is gaining increasing popularity in the medical domain for analyzing medical images, which is considered an effective technique to safeguard sensitive patient data and comply with privacy regulations. However, several recent studies have revealed that the default settings of FL may leak private training data under privacy attacks. Thus, it is still unclear whether and to what extent such privacy risks of FL exist in the medical domain, and if so, "how to mitigate such risks?". In this paper, first, we propose a holistic framework for Medical data Privacy risk analysis and mitigation in Federated Learning (MedPFL) to analyze privacy risks and develop effective mitigation strategies in FL for protecting private medical data. Second, we demonstrate the substantial privacy risks of using FL to process medical images, where adversaries can easily perform privacy attacks to reconstruct private medical images accurately. Third, we show that the defense approach of adding random noises may not always work effectively to protect medical images against privacy attacks in FL, which poses unique and pressing challenges associated with medical data for privacy protection.