A New Federated Learning Framework Against Gradient Inversion Attacks
Authors: name: Pengxin Guo, name: Shuang Zeng, name: Wenhao Chen, name: Xiaodan Zhang, name: Weihong Ren, name: Yuyin Zhou, name: Liangqiong Qu

Federated Learning (FL) aims to protect data privacy by enabling clients to
collectively train machine learning models without sharing their raw data.
However, recent studies demonstrate that information exchanged during FL is
subject to Gradient Inversion Attacks (GIA) and, consequently, a variety of
privacy-preserving methods have been integrated into FL to thwart such attacks,
such as Secure Multi-party Computing (SMC), Homomorphic Encryption (HE), and
Differential Privacy (DP). Despite their ability to protect data privacy, these
approaches inherently involve substantial privacy-utility trade-offs. By
revisiting the key to privacy exposure in FL under GIA, which lies in the
frequent sharing of model gradients that contain private data, we take a new
perspective by designing a novel privacy preserve FL framework that effectively
``breaks the direct connection'' between the shared parameters and the local
private data to defend against GIA. Specifically, we propose a Hypernetwork
Federated Learning (HyperFL) framework that utilizes hypernetworks to generate
the parameters of the local model and only the hypernetwork parameters are
uploaded to the server for aggregation. Theoretical analyses demonstrate the
convergence rate of the proposed HyperFL, while extensive experimental results
show the privacy-preserving capability and comparable performance of HyperFL.
Code is available at https://github.com/Pengxin-Guo/HyperFL.