Training Large-Vocabulary Neural Language Models by Private Federated Learning for Resource-Constrained Devices
Abstract
Federated Learning (FL) is a technique to train models using data distributed across devices. Differential Privacy (DP) provides a formal privacy guarantee for sensitive data. Our goal is to train a large neural network language model (NNLM) on compute-constrained devices while preserving privacy using FL and DP. However, the DP-noise introduced to the model increases as the model size grows, which often prevents convergence. We propose Partial Embedding Updates (PEU), a novel technique to decrease noise by decreasing payload size. Furthermore, we adopt Low Rank Adaptation (LoRA) and Noise Contrastive Estimation (NCE) to reduce the memory demands of large models on compute-constrained devices. This combination of techniques makes it possible to train large-vocabulary language models while preserving accuracy and privacy.
Keywords
Cite
@article{arxiv.2207.08988,
title = {Training Large-Vocabulary Neural Language Models by Private Federated Learning for Resource-Constrained Devices},
author = {Mingbin Xu and Congzheng Song and Ye Tian and Neha Agrawal and Filip Granqvist and Rogier van Dalen and Xiao Zhang and Arturo Argueta and Shiyi Han and Yaqiao Deng and Leo Liu and Anmol Walia and Alex Jin},
journal= {arXiv preprint arXiv:2207.08988},
year = {2022}
}