SASH: Efficient Secure Aggregation Based on SHPRG For Federated Learning

Zizhen Liu, Si Chen, Jing Justin Ye, Junfeng Fan, Huawei Li, Xiaowei Li

Keywords: Federated Learning, Privacy Security, Efficient

TL;DR: we propose a novel secure aggregation scheme based on seed homomorphic pseudo-random generator (SHPRG) to prevent private training data leakage in Federated Learning systems.

Abstract: To prevent private training data leakage in Federated Learning systems, we propose a novel secure aggregation scheme based on seed homomorphic pseudo-random generator (SHPRG), named SASH. SASH leverages the homomorphic property of SHPRG to simplify the masking and demasking scheme, which for each of the clients and for the server, entails a overhead linear w.r.t model size and constant w.r.t number of clients. We prove that even against worst-case colluding adversaries, SASH preserves training data privacy, while being resilient to dropouts without extra overhead. We experimentally demonstrate SASH significantly improves the efficiency to 20× over baseline, especially in the more realistic case where the numbers of clients and model size become large, and a certain percentage of clients drop out from the system.