MPCFormer: fast, performant and private Transformer inference with MPC
Abstract
Enabling private inference is crucial for many cloud inference services that are based on Transformer models. However, existing private inference solutions can increase the inference latency by more than 60x or significantly compromise the inference quality. In this paper, we design the framework MPCFORMER as a practical solution, using Secure Multi-Party Computation (MPC) and Knowledge Distillation (KD). Through extensive evaluations, we show that MPCFORMER significantly speeds up Transformer inference in MPC settings while achieving similar ML performance to the input model. On the IMDb dataset, it achieves similar performance to BERTBASE, while being 5.3x faster. On the GLUE benchmark, it achieves 97% performance of BERTBASE with a 2.2x speedup. MPCFORMER remains effective with different trained Transformer weights such as ROBERTABASE and larger models including BERTLarge. Code is available at https://github.com/MccRee177/MPCFormer.
Keywords
Cite
@article{arxiv.2211.01452,
title = {MPCFormer: fast, performant and private Transformer inference with MPC},
author = {Dacheng Li and Rulin Shao and Hongyi Wang and Han Guo and Eric P. Xing and Hao Zhang},
journal= {arXiv preprint arXiv:2211.01452},
year = {2023}
}