  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 models by taking
advantage of public data, especially for customized on-device architectures
that do not have ready-to-use pre-trained models.
