Instruction tuning has been identified as a crucial technique for optimizing the performance of large language models (LLMs) in generating human-aligned responses. Nonetheless, gathering diversified and superior-quality instruction data for such tuning presents notable obstacles, especially in domains with rigid privacy provisions. Federated instruction tuning (FedIT) has emerged as a promising solution, by consolidating collaborative training across multiple data owners, thereby resulting in a privacy-preserving learning model. However, FedIT encounters limitations such as scarcity of instructional data and risk of exposure to training data extraction attacks. In this paper, we propose a novel federated algorithm, FewFedPIT, designed to simultaneously enhance privacy protection and model performance of federated few-shot learning. FewFedPITcomprises three vital components on the client side: (1) synthetic data generation, which utilizes LLMs' in-context learning capacity to generate synthetic data autonomously, thus expanding the local database; (2) parameter isolation training, which individually updates the public parameters in the synthetic data and the private parameters in the local data, consequently mitigating the noise impact of the synthetic data; (3) local aggregation sharing, which mixes public and private parameters before uploading, effectively preventing data extraction attacks. Extensive experiments on three open-source datasets demonstrate the effectiveness of FewFedPITin, enhancing privacy preservation and improving federated few-shot performance.

翻译：指令调优已被视为优化大语言模型生成与人类对齐响应的关键技术。然而，为此类调优收集多样化且高质量的指令数据面临显著障碍，尤其是在隐私规定严格的领域。联邦指令调优通过整合多个数据所有者间的协作训练，成为一种有前景的解决方案，从而产生隐私保护的学习模型。然而，联邦指令调优面临诸如指令数据稀缺以及训练数据提取攻击风险等限制。本文提出一种新颖的联邦算法FewFedPIT，旨在同时提升联邦少样本学习的隐私保护与模型性能。FewFedPIT在客户端包含三个关键组件：（1）合成数据生成，利用大语言模型的上下文学习能力自主生成合成数据，从而扩展本地数据库；（2）参数隔离训练，分别更新合成数据中的公共参数与本地数据中的私有参数，从而减轻合成数据的噪声影响；（3）本地聚合共享，在上传前混合公共与私有参数，有效防止数据提取攻击。在三个开源数据集上的大量实验证明了FewFedPIT在增强隐私保护与提升联邦少样本性能方面的有效性。