A Fast Federated Learning-based Crypto-aggregation Scheme and Its Simulation Analysis

To solve the problem of increased computation and communication costs caused by using homomorphic encryption (HE) to protect all gradients in traditional cryptographic aggregation (crypto-aggregation) schemes, a fast crypto-aggregation scheme called RandomCrypt was proposed. RandomCrypt performed clipping and quantization to fix the range of gradient values and then added two types of noise on the gradient for encryption and differential privacy (DP) protection. It conducted HE on noise keys to revise the precision loss caused by DP protection. RandomCrypt was implemented based on a FATE framework, and a hacking simulation experiment was conducted. The results show that the proposed scheme can effectively hinder inference attacks while ensuring training accuracy. It only requires 45%~51% communication cost and 5%~23% computation cost compared with traditional schemes.