TGSFL: Trustworthy group-signature-based federated learning for LLMs in healthcare

Abstract

Federated learning (FL) enables collaborative model training without sharing raw data, making it well suited to privacy-sensitive healthcare applications. However, malicious model updates and anonymous participation abuse can disrupt optimization and undermine the reliability of downstream medical AI systems. Existing defenses often address robustness or privacy separately, with limited support for accountable authentication and client revocation. To address these limitations, we propose TGSFL, a Trustworthy Group-Signature-Based Federated Learning framework for healthcare large language models (LLMs). The proposed framework integrates dynamic group-signature authentication, per-client local differential privacy, and dual-layer malicious-update detection to identify and suppress adversarial updates before aggregation. It further supports anonymous yet traceable participation and dynamic revocation of persistently malicious clients. Theoretical analysis establishes conditional traceability, dynamic revocation, and per-client local differential privacy. Experiments under multiple Byzantine attack scenarios show that TGSFL improves robustness and training stability over existing baselines while introducing only modest computational overhead, demonstrating its practicality for secure and privacy-preserving medical AI training.