Federated Adversarial Training for Privacy-Preserving Robust Large Language Model Agents in Distributed Medical Decision Support Systems
Keywords:
federated learning, adversarial training, large language models, medical decision support, privacy preservation, robustness, distributed systems, healthcare AIAbstract
The rapid integration of large language model (LLM) agents into clinical decision support systems promises transformative advances in diagnostic accuracy, treatment personalization, and operational efficiency. However, the deployment of such systems across distributed healthcare networks introduces profound challenges at the intersection of data privacy, model robustness, and regulatory compliance. Centralizing sensitive patient data for training is often infeasible under frameworks such as HIPAA and GDPR, while LLM agents remain vulnerable to adversarial manipulations that can induce harmful clinical errors. This paper presents a comprehensive system-level investigation of federated adversarial training as a unified paradigm for cultivating privacy-preserving yet robust LLM agents within distributed medical decision support infrastructures. We analyze the architectural design space encompassing secure aggregation, differential privacy, and adversarial example generation orchestrated across heterogeneous clinical sites. The discussion extends to structural trade-offs between communication efficiency, model utility, and resilience to both data-poisoning and evasion attacks in the linguistic domain. We examine the complex interplay between adversarial robustness mechanisms and privacy leakage, highlighting how federated optimization can amplify or mitigate membership inference risks. Furthermore, we address infrastructure sustainability, computational resource allocation, edge-cloud orchestration, and the carbon footprint of continuously retraining medical LLM agents. Governance challenges, fairness across demographically diverse populations, and the alignment of federated adversarial training with evolving regulatory instruments for AI-based medical devices are critically evaluated. The paper concludes by outlining forward-looking policy and design recommendations to bridge the gap between theoretical robustness guarantees and operational medical realities.
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