提升联邦大模型抗干扰能力，保护隐私同时增强鲁棒性
FedEAT: A Robustness Optimization Framework for Federated LLMs
- 在客户端嵌入空间进行对抗训练，提升模型泛化能力
- 采用几何中位数聚合，有效抵御恶意客户端攻击
- 适用于医疗、金融等敏感数据场景的联邦学习应用
大语言模型（LLMs）在自然语言理解与内容生成方面取得显著进展，但仍面临计算成本高和训练数据不足的问题。将联邦学习（FL）与大语言模型结合（联邦大模型），可在保护隐私的前提下利用分布式数据，适用于医疗、金融等敏感领域。然而，联邦大模型仍存在数据异质性、恶意客户端及对抗攻击等鲁棒性挑战。本文首次系统分析联邦大模型的鲁棒性问题，提出FedEAT（联邦嵌入空间对抗训练）框架，在客户端模型的嵌入空间实施对抗训练，并采用几何中位数聚合策略提升整体鲁棒性。实验表明，该方法在显著增强鲁棒性的同时，仅带来微小性能损失。
原文摘要 · Abstract (English)
Significant advancements have been made by Large Language Models (LLMs) in the domains of natural language understanding and automated content creation. However, they still face persistent problems, including substantial computational costs and inadequate availability of training data. The combination of Federated Learning (FL) and LLMs (federated LLMs) offers a solution by leveraging distributed data while protecting privacy, which positions it as an ideal choice for sensitive domains. However, Federated LLMs still suffer from robustness challenges, including data heterogeneity, malicious clients, and adversarial attacks, which greatly hinder their applications. We first introduce the robustness problems in federated LLMs, to address these challenges, we propose FedEAT (Federated Embedding space Adversarial Training), a novel framework that applies adversarial training in the embedding space of client LLM and employs a robust aggregation approach, specifically geometric median aggregation, to enhance the robustness of Federated LLMs. Our experiments demonstrate that FedEAT effectively improves the robustness of Federated LLMs with minimal performance loss.
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