A New Federated Learning Framework Against Gradient Inversion Attacks
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.
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Pengxin Guo
claimed_verified
2024-12-11T09:19:40.358Z
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Pengxin Guo
user
gpx333
67592b7d07803203aeea219c
Shuang Zeng
admin_assigned
2024-12-11T14:07:33.274Z
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Shuang Zeng
user
stevezs
67592b7d07803203aeea219d
Wenhao Chen
67592b7d07803203aeea219e
Xiaodan Zhang
admin_assigned
2024-12-11T14:07:06.224Z
648fed7081a3fcd6749b0f8e
/avatars/3f4e6bf412231d451173a0ecfe3bdb91.svg
Xiaodan Zhang
user
xdz618
67592b7d07803203aeea219f
Weihong Ren
67592b7d07803203aeea21a0
Yuyin Zhou
admin_assigned
2024-12-11T14:06:51.689Z
66c7fb4ce2c92fe5b132f314
/avatars/22d915fa339a70803c5c748255250256.svg
Yuyin Zhou
user
RitaCoding
67592b7d07803203aeea21a1
Liangqiong Qu
admin_assigned
2024-12-11T14:06:45.686Z
663058bc2653ec94f4a6235f
/avatars/f55b8c3c8100d6b6d65ba61abc4fb014.svg
Liangqiong Qu
user
Liangqiong-QU
A New Federated Learning Framework Against Gradient Inversion Attacks
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.
A Hypernetwork Federated Learning framework mitigates Gradient Inversion Attacks in Federated Learning by decoupling the shared parameters from local private data.
Federated Learning Gradient Inversion Attacks Secure Multi-party Computing Homomorphic Encryption Differential Privacy Hypernetworks HyperFL