Federated Adversarial Training of Large Language Model Agents for Distributed Healthcare Systems
Keywords:
federated learning, adversarial training, large language models, healthcare systems, distributed infrastructure, robustness, governance, privacy, clinical decision support, socio-technical systemsAbstract
The integration of large language models into distributed healthcare infrastructures introduces unprecedented capabilities for clinical decision support, patient communication, and administrative automation, yet simultaneously exposes these systems to adversarial vulnerabilities that can undermine patient safety and data integrity. This paper proposes a federated adversarial training framework specifically designed for large language model agents operating across decentralized healthcare networks. The framework orchestrates collaborative adversarial training among multiple institutional nodes while preserving strict data locality constraints imposed by healthcare privacy regulations. We examine the architectural trade-offs between model robustness, communication efficiency, and governance compliance within a federated paradigm, emphasizing the role of adversarial example generation and distribution as a shared public good. The paper further analyzes the structural implications of deploying such agents in real-world clinical environments, including the need for continuous monitoring, fairness across heterogeneous patient populations, and the mitigation of distribution shift from participating institutions. Policy-oriented considerations regarding certification of robustness, liability for adversarial failures, and the socioeconomic barriers to participation are discussed. Through a cross-domain comparison with federated learning in other sensitive domains, we identify the unique challenges posed by the generative and interactive nature of language models. The paper concludes with a forward-looking perspective on sustainable adversarial defense mechanisms that balance utility, privacy, and equity in federated healthcare systems. This work contributes a system-level blueprint for the robust and responsible deployment of large language model agents in distributed critical-care environments.
References
1. McMahan, B., Moore, E., Ramage, D., Hampson, S., & y Arcas, B. A. (2017). Communication-efficient learning of deep networks from decentralized data. In Artificial Intelligence and Statistics (pp. 1273–1282). PMLR.
2. Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877–1901.
3. Wallace, E., Feng, S., Kandpal, N., Gardner, M., & Singh, S. (2019). Universal adversarial triggers for attacking and analyzing NLP. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (pp. 2153–2162).
4. Jin, D., Jin, Z., Zhou, J. T., & Szolovits, P. (2020). Is BERT really robust? A strong baseline for natural language attack on text classification and entailment. In Proceedings of the AAAI Conference on Artificial Intelligence, 34(05), 8018–8025.
5. Bagdasaryan, E., Veit, A., Hua, Y., Estrin, D., & Shmatikov, V. (2020). How to backdoor federated learning. In International Conference on Artificial Intelligence and Statistics (pp. 2938–2948). PMLR.
6. Price, W. N., & Cohen, I. G. (2019). Privacy in the age of medical big data. Nature Medicine, 25(1), 37–43.
7. Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020). Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine, 37(3), 50–60.
8. Rieke, N., Hancox, J., Li, W., Milletari, F., Roth, H. R., Albarqouni, S., ... & Bakas, S. (2020). The future of digital health with federated learning. NPJ Digital Medicine, 3(1), 1–7.
9. Sheller, M. J., Edwards, B., Reina, G. A., Martin, J., Pati, S., Kotrotsou, A., ... & Bakas, S. (2020). Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data. Scientific Reports, 10(1), 1–12.
10. Madry, A., Makelov, A., Schmidt, L., Tsipras, D., & Vladu, A. (2018). Towards deep learning models resistant to adversarial attacks. In International Conference on Learning Representations.
11. Hu, S. (2026). Research on Security Enhancement Methods for Adversarial Robust Large Language Model Intelligent Agents for Medical Decision-Making Tasks. arXiv preprint arXiv:2605.08257.
12. Ebrahimi, J., Rao, A., Lowd, D., & Dou, D. (2018). HotFlip: White-box adversarial examples for text classification. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (pp. 31–36).
13. Shah, A., Bhagoji, A. N., Chaterji, S., & Huang, F. (2022). Federated adversarial training with multi-objective optimization. In Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security (pp. 2721–2734).
14. Zizzo, G., Rawat, A., & Sinn, M. (2020). Federated adversarial training for robust machine learning. In International Workshop on Federated Learning for User Privacy and Data Confidentiality at ICML.
15. Floridi, L., & Cowls, J. (2019). A unified framework of five principles for AI in society. Harvard Data Science Review, 1(1).
16. Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389–399.
17. Blanchard, P., El Mhamdi, E. M., Guerraoui, R., & Stainer, J. (2017). Machine learning with adversaries: Byzantine tolerant gradient descent. Advances in Neural Information Processing Systems, 30.
18. Wong, E., & Kolter, J. Z. (2018). Provable defenses against adversarial examples via the convex outer adversarial polytope. In International Conference on Machine Learning (pp. 5283–5292). PMLR.
19. Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., & Zhang, L. (2016). Deep learning with differential privacy. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (pp. 308–318).
20. Mohri, M., Sivek, G., & Suresh, A. T. (2019). Agnostic federated learning. In International Conference on Machine Learning (pp. 4615–4625). PMLR.
21. Finn, C., Abbeel, P., & Levine, S. (2017). Model-agnostic meta-learning for fast adaptation of deep networks. In International Conference on Machine Learning (pp. 1126–1135). PMLR.
22. Houlsby, N., Giurgiu, A., Jastrzebski, S., Morrone, B., De Laroussilhe, Q., Gesmundo, A., ... & Gelly, S. (2019). Parameter-efficient transfer learning for NLP. In International Conference on Machine Learning (pp. 2790–2799). PMLR.
23. Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206–215.
24. Papernot, N., McDaniel, P., Wu, X., Jha, S., & Swami, A. (2016). Distillation as a defense to adversarial perturbations against deep neural networks. In IEEE Symposium on Security and Privacy (pp. 582–597).
25. Kurakin, A., Goodfellow, I., & Bengio, S. (2017). Adversarial machine learning at scale. In International Conference on Learning Representations.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Computational Intelligence Systems
This work is licensed under a Creative Commons Attribution 4.0 International License.
This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
