Differentially Private Knowledge Distillation via Synthetic Text
  Generation
James Flemings Murali Annavaram
Large Language models (LLMs) are achieving state-of-the-art performance in
many different downstream tasks. However, the increasing urgency of data
privacy requires LLMs to train with Differential Privacy (DP) on private data.
Concurrently it is also necessary to compress LLMs for real-life deployments on
resource-constrained devices or latency-sensitive applications. Differential
privacy and model compression generally must trade off utility loss to achieve
their objectives. Moreover, concurrently achieving both can result in even more
utility loss. To this end, we propose a novel differentially private knowledge
distillation algorithm that exploits synthetic data generated by a
differentially private LLM. The knowledge of a teacher model is transferred
onto the student in two ways: one way from the synthetic data itself, the hard
labels, and the other way by the output distribution of the teacher model
evaluated on the synthetic data, the soft labels. Furthermore, if the teacher
and student share a similar architectural structure, we can further distill
knowledge by exploiting hidden representations. Our results show that our
framework substantially improves the utility over existing baselines with
strong privacy parameters, {\epsilon} = 2, validating that we can successfully
compress autoregressive LLMs while preserving the privacy of training data.