title: A New Federated Learning Framework Against Gradient Inversion Attacks
summary: 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.
paper_authors._id: 67592b7d07803203aeea219b
paper_authors.name: Pengxin Guo
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-11T09:19:40.358Z
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paper_authors.avatarUrl: /avatars/fa0d328300b03bcbbf9b3a7532f28458.svg
paper_authors.fullname: Pengxin Guo
paper_authors.type: user
paper_authors.user: gpx333
paper_authors._id: 67592b7d07803203aeea219c
paper_authors.name: Shuang Zeng
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/717b96ddef8a4c19ce07ea1fd9e9fd66.svg
paper_authors.fullname: Shuang Zeng
paper_authors.type: user
paper_authors.user: stevezs
paper_authors._id: 67592b7d07803203aeea219d
paper_authors.name: Wenhao Chen
paper_authors._id: 67592b7d07803203aeea219e
paper_authors.name: Xiaodan Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-11T14:07:06.224Z
paper_authors._id: 648fed7081a3fcd6749b0f8e
paper_authors.avatarUrl: /avatars/3f4e6bf412231d451173a0ecfe3bdb91.svg
paper_authors.fullname: Xiaodan Zhang
paper_authors.type: user
paper_authors.user: xdz618
paper_authors._id: 67592b7d07803203aeea219f
paper_authors.name: Weihong Ren
paper_authors._id: 67592b7d07803203aeea21a0
paper_authors.name: Yuyin Zhou
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-11T14:06:51.689Z
paper_authors._id: 66c7fb4ce2c92fe5b132f314
paper_authors.avatarUrl: /avatars/22d915fa339a70803c5c748255250256.svg
paper_authors.fullname: Yuyin Zhou
paper_authors.type: user
paper_authors.user: RitaCoding
paper_authors._id: 67592b7d07803203aeea21a1
paper_authors.name: Liangqiong Qu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-11T14:06:45.686Z
paper_authors._id: 663058bc2653ec94f4a6235f
paper_authors.avatarUrl: /avatars/f55b8c3c8100d6b6d65ba61abc4fb014.svg
paper_authors.fullname: Liangqiong Qu
paper_authors.type: user
paper_authors.user: Liangqiong-QU
paper_title: A New Federated Learning Framework Against Gradient Inversion Attacks
paper_summary: 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.
paper_ai_summary: A Hypernetwork Federated Learning framework mitigates Gradient Inversion Attacks in Federated Learning by decoupling the shared parameters from local private data.
paper_ai_keywords: Federated Learning Gradient Inversion Attacks Secure Multi-party Computing Homomorphic Encryption Differential Privacy Hypernetworks HyperFL