# Exploring adversarial attacks in federated learning for medical imaging

arXiv: https://arxiv.org/abs/2310.06227 · HTML: https://mlsanity.com/paper/2310.06227/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 42 references that are in the map.
- First posted on arXiv 10 Oct 2023.

## Builds on (most cited first, 21 of 21)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Adversarial examples in the physical world](https://mlsanity.com/paper/1607.02533/) (2016)
- [Theoretically Principled Trade-off between Robustness and Accuracy](https://mlsanity.com/paper/1901.08573/) (2019)
- [Evasion Attacks against Machine Learning at Test Time](https://mlsanity.com/paper/1708.06131/) (2017)
- [Federated Learning with Differential Privacy: Algorithms and Performance Analysis](https://mlsanity.com/paper/1911.00222/) (2019)
- [Rotation Equivariant CNNs for Digital Pathology](https://mlsanity.com/paper/1806.03962/) (2018)
- [Can You Really Backdoor Federated Learning?](https://mlsanity.com/paper/1911.07963/) (2019)
- [Adversarial Attacks and Defenses in Images, Graphs and Text: A Review](https://mlsanity.com/paper/1909.08072/) (2019)
- [Understanding Adversarial Attacks on Deep Learning Based Medical Image Analysis Systems](https://mlsanity.com/paper/1907.10456/) (2019)
- [Decoupling Direction and Norm for Efficient Gradient-Based L2 Adversarial Attacks and Defenses](https://mlsanity.com/paper/1811.09600/) (2018)
- [Adversarial Robustness vs Model Compression, or Both?](https://mlsanity.com/paper/1903.12561/) (2019)
- [Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learning](https://mlsanity.com/paper/2106.03328/) (2021)
- [Backdoor Attacks and Defenses in Federated Learning: Survey, Challenges and Future Research Directions](https://mlsanity.com/paper/2303.02213/) (2023)
- [Distributionally Adversarial Attack](https://mlsanity.com/paper/1808.05537/) (2018)
- [Assessing Threat of Adversarial Examples on Deep Neural Networks](https://mlsanity.com/paper/1610.04256/) (2016)
- [A Bi-layered Parallel Training Architecture for Large-scale Convolutional Neural Networks](https://mlsanity.com/paper/1810.07742/) (2018)
- [Stochastic Channel-Based Federated Learning for Medical Data Privacy Preserving](https://mlsanity.com/paper/1910.11160/) (2019)
- [A Comparative Study of Federated Learning Models for COVID-19 Detection](https://mlsanity.com/paper/2303.16141/) (2023)
- [AI Technical Considerations: Data Storage, Cloud usage and AI Pipeline](https://mlsanity.com/paper/2201.08356/) (2022)

## Built on by (most cited first, 8 of 8)

- [Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challenges](https://mlsanity.com/paper/2407.06754/) (2024)
- [FlexOlmo: Open Language Models for Flexible Data Use](https://mlsanity.com/paper/2507.07024/) (2025)
- [The Hidden Adversarial Vulnerabilities of Medical Federated Learning](https://mlsanity.com/paper/2310.13893/) (2023)
- [Accelerated Training of Federated Learning via Second-Order Methods](https://mlsanity.com/paper/2505.23588/) (2025)
- [Autonomous Cyber Resilience via a Co-Evolutionary Arms Race within a Fortified Digital Twin Sandbox](https://mlsanity.com/paper/2506.20102/) (2025)
- [OptiGradTrust: Byzantine-Robust Federated Learning with Multi-Feature Gradient Analysis and Reinforcement Learning-Based Trust Weighting](https://mlsanity.com/paper/2507.23638/) (2025)
- [Lightweight and Robust Federated Data Valuation](https://mlsanity.com/paper/2509.25560/) (2025)
- [MetaMoE: Diversity-Aware Proxy Selection for Privacy-Preserving Mixture-of-Experts Unification](https://mlsanity.com/paper/2605.14289/) (2026)

## Abstract (arXiv)

Federated learning offers a privacy-preserving framework for medical image analysis but exposes the system to adversarial attacks. This paper aims to evaluate the vulnerabilities of federated learning networks in medical image analysis against such attacks. Employing domain-specific MRI tumor and pathology imaging datasets, we assess the effectiveness of known threat scenarios in a federated learning environment. Our tests reveal that domain-specific configurations can increase the attacker's success rate significantly. The findings emphasize the urgent need for effective defense mechanisms and suggest a critical re-evaluation of current security protocols in federated medical image analysis systems.