MPCFormer: fast, performant and private Transformer inference with MPC

Understand

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.