{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/pre-text-training-language-models-on-private","title":"PrE-Text: Training Language Models on Private Federated Data in the Age of LLMs","arxiv_id":"2406.02958","date":"2024-06-05","proceeding":null,"authors":["Charlie Hou","Akshat Shrivastava","Hongyuan Zhan","Rylan Conway","Trang Le","Adithya Sagar","Giulia Fanti","Daniel Lazar"],"abstract":"On-device training is currently the most common approach for training machine learning (ML) models on private, distributed user data. Despite this, on-device training has several drawbacks: (1) most user devices are too small to train large models on-device, (2) on-device training is communication- and computation-intensive, and (3) on-device training can be difficult to debug and deploy. To address these problems, we propose Private Evolution-Text (PrE-Text), a method for generating differentially private (DP) synthetic textual data. First, we show that across multiple datasets, training small models (models that fit on user devices) with PrE-Text synthetic data outperforms small models trained on-device under practical privacy regimes ($\\epsilon=1.29$, $\\epsilon=7.58$). We achieve these results while using 9$\\times$ fewer rounds, 6$\\times$ less client computation per round, and 100$\\times$ less communication per round. Second, finetuning large models on PrE-Text's DP synthetic data improves large language model (LLM) performance on private data across the same range of privacy budgets. Altogether, these results suggest that training on DP synthetic data can be a better option than training a model on-device on private distributed data. 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