FewFedPIT: Towards Privacy-preserving and Few-shot Federated Instruction Tuning
<|reference_start|>FewFedPIT: Towards Privacy-preserving and Few-shot Federated Instruction Tuning: 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.<|reference_end|>
@article{zhang2024fewfedpit:,
    title={FewFedPIT: Towards Privacy-preserving and Few-shot Federated Instruction
  Tuning},
    author={Zhuo Zhang, Jingyuan Zhang, Jintao Huang, Lizhen Qu, Hongzhi Zhang,
  Qifan Wang, Xun Zhou, Zenglin Xu},
    journal={arXiv preprint arXiv:2403.06131},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.06131},
    primaryClass={cs.CR cs.AI}
}