We study (differentially) private federated learning (FL) of language models. The language models in cross-device FL are relatively small, which can be trained with meaningful formal user-level differential privacy (DP) guarantees when massive parallelism in training is enabled by the participation of a moderate size of users. Recently, public data has been used to improve privacy-utility trade-offs for both large and small language models. In this work, we provide a systematic study of using large-scale public data and LLMs to help differentially private training of on-device FL models, and further improve the privacy-utility tradeoff by techniques of distillation. Moreover, we propose a novel distribution matching algorithm with theoretical grounding to sample public data close to private data distribution, which significantly improves the sample efficiency of (pre-)training on public data. The proposed method is efficient and effective for training private model by taking advantage of public data, especially for customized on-device architectures that do not have ready-to-use pre-trained models.

本文研究基于大规模公共数据和语言模型的差分隐私联邦学习，提出了一种新的分布匹配算法，以样本高效为目标，实现了基于公共数据训练私有模型的高效和有效的方法。

公共大型语言模型能帮助私人跨设备联邦学习吗？