Large Language Models (LLMs), with their advanced contextual understanding abilities, have demonstrated considerable potential in enhancing recommendation systems via fine-tuning methods. However, fine-tuning requires users' behavior data, which poses considerable privacy risks due to the incorporation of sensitive user information. The unintended disclosure of such data could infringe upon data protection laws and give rise to ethical issues. To mitigate these privacy issues, Federated Learning for Recommendation (Fed4Rec) has emerged as a promising approach. Nevertheless, applying Fed4Rec to LLM-based recommendation presents two main challenges: first, an increase in the imbalance of performance across clients, affecting the system's efficiency over time, and second, a high demand on clients' computational and storage resources for local training and inference of LLMs. To address these challenges, we introduce a Privacy-Preserving LLM-based Recommendation (PPLR) framework. The PPLR framework employs two primary strategies. First, it implements a dynamic balance strategy, which involves the design of dynamic parameter aggregation and adjustment of learning speed for different clients during the training phase, to ensure relatively balanced performance across all clients. Second, PPLR adopts a flexible storage strategy, selectively retaining certain sensitive layers of the language model on the client side while offloading non-sensitive layers to the server. This approach aims to preserve user privacy while efficiently saving computational and storage resources. Experimental results demonstrate that PPLR not only achieves a balanced performance among clients but also enhances overall system performance in a manner that is both computationally and storage-efficient, while effectively protecting user privacy.

翻译：大语言模型凭借其先进的上下文理解能力，通过微调方法在提升推荐系统性能方面展现出巨大潜力。然而，微调过程需要使用用户行为数据，其中包含敏感用户信息，存在显著隐私风险。此类数据的意外泄露可能违反数据保护法规并引发伦理问题。为解决这些隐私问题，联邦推荐学习（Fed4Rec）已成为一种具有前景的方法。但将Fed4Rec应用于基于大语言模型的推荐面临两大挑战：首先，客户端间性能不平衡加剧，影响系统长期运行效率；其次，客户端需承担本地训练和推理大语言模型的高计算与存储资源需求。针对这些挑战，我们提出隐私保护的基于大语言模型推荐框架（PPLR）。该框架采用两大核心策略：其一，实施动态平衡策略，在训练阶段设计动态参数聚合机制并调整不同客户端的学习速率，确保所有客户端保持相对均衡的性能；其二，采用灵活存储策略，在客户端侧选择性保留语言模型的敏感层，将非敏感层卸载至服务器端。该方法在保护用户隐私的同时，可高效节省计算与存储资源。实验结果表明，PPLR不仅实现了客户端间性能均衡，还能在有效保护用户隐私的前提下，以计算与存储高效的方式提升整体系统性能。