Faster Secure Multiparty Computation of Adaptive Gradient Descent
Wenjie Lu, Yixuan Fang, Zhicong Huang, Cheng Hong, Chaochao Chen, Hunter Qu, Yajin Zhou, Kui Ren · 2020
Most of the secure multi-party computation (MPC) machine learning methods can only afford simple gradient descent (sGD 1) optimizers, and are unable to benefit from the recent progress of adaptive GD optimizers (e.g., Adagrad, Adam and their variants), which include square-root and reciprocal operations that are hard to compute in MPC. To mitigate this issue, we introduce InvertSqrt, an efficient MPC protocol for computing 1/√x. Then we implement the Adam adaptive GD optimizer based on InvertSqrt and use it for training on different datasets. The training costs compare favorably to the sGD ones, indicating that adaptive GD optimizers in MPC have become practical.