用联邦学习保护隐私，让大模型分析心理健康更安全
FedMentalCare: Towards Privacy-Preserving Fine-Tuned LLMs to Analyze Mental Health Status Using Federated Learning Framework
- 联邦学习+低秩适配，本地训练不传数据
- 不同模型和数据量下仍保持稳定效果
- 适合医疗、隐私敏感场景的AI应用
随着全球心理健康问题日益普遍，基于AI的聊天机器人成为可及的心理健康支持工具。然而，在心理健康应用中部署大语言模型（LLMs）带来显著隐私风险，尤其涉及HIPAA和GDPR等法规。本文提出FedMentalCare，一种结合联邦学习（FL）与低秩适配（LoRA）的隐私保护框架，用于在不共享原始数据的前提下微调LLMs以分析心理健康状态。我们研究了客户端数据量差异及模型架构（如MobileBERT和MiniLM）对联邦学习环境性能的影响。结果表明，该框架在保障数据安全的同时，具备可扩展性和计算高效性，适用于真实世界心理健康服务中的大模型部署。
原文摘要 · Abstract (English)
With the increasing prevalence of mental health conditions worldwide, AI-powered chatbots and conversational agents have emerged as accessible tools to support mental health. However, deploying Large Language Models (LLMs) in mental healthcare applications raises significant privacy concerns, especially regarding regulations like HIPAA and GDPR. In this work, we propose FedMentalCare, a privacy-preserving framework that leverages Federated Learning (FL) combined with Low-Rank Adaptation (LoRA) to fine-tune LLMs for mental health analysis. We investigate the performance impact of varying client data volumes and model architectures (e.g., MobileBERT and MiniLM) in FL environments. Our framework demonstrates a scalable, privacy-aware approach for deploying LLMs in real-world mental healthcare scenarios, addressing data security and computational efficiency challenges.
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