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A Durable Backdoor Attack on Medical Imaging via Federated Learning.

Hichem Faraoun¹, Reda Bellafqira^1,2, Gouenou Coatrieux^1,2

¹IMT Atlantique, 29238 Brest Cedex, France.

Studies in Health Technology and Informatics

|May 23, 2026

Summary

Federated Learning (FL) is vulnerable to backdoor attacks. This study introduces a durable attack using Generative Adversarial Networks, highlighting the need for robust defenses in medical AI.

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Area of Science:

Artificial Intelligence
Machine Learning
Medical Informatics

Background:

Federated Learning (FL) facilitates collaborative model training across healthcare institutions without raw data sharing, enhancing patient privacy.
The distributed and semi-trusted nature of FL systems makes them susceptible to sophisticated backdoor attacks, compromising model integrity.
Backdoor attacks involve malicious clients injecting hidden triggers into models, causing misclassifications during inference.

Purpose of the Study:

To propose a novel, durable backdoor attack specifically designed for Federated Learning environments in healthcare.
To enhance the robustness and persistence of backdoor effects even with limited attacker participation.
To underscore the critical need for advanced defense mechanisms against such attacks in medical AI applications.

Keywords:

Backdoor Attacks Federated Learning Medical Imaging AI

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Main Methods:

Developed a backdoor attack leveraging a Generative Adversarial Network (GAN) guided by the global FL model.
Utilized the GAN to generate synthetic data that mimics the distribution of benign client data, masking the attack.
Implemented a two-step strategy to increase the resilience and durability of the injected backdoor.

Main Results:

The proposed attack successfully created a durable backdoor effect within the FL model.
The backdoor persisted effectively even when the malicious client's participation was limited.
Experiments conducted on the MedMNIST benchmark under non-IID data distributions validated the attack's efficacy in realistic medical scenarios.

Conclusions:

The study demonstrates a potent and durable backdoor attack against Federated Learning in medical applications.
Existing FL security measures may be insufficient against this advanced attack vector.
There is an urgent requirement for developing and implementing more resilient defense strategies to safeguard the trustworthiness of FL in healthcare.