# A New Federated Learning Framework Against Gradient Inversion Attacks

arXiv: https://arxiv.org/abs/2412.07187 · HTML: https://mlsanity.com/paper/2412.07187/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 91 references that are in the map.
- First posted on arXiv 10 Dec 2024.

## Builds on (most cited first, 12 of 12)

- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [Empirical Evaluation of Rectified Activations in Convolutional Network](https://mlsanity.com/paper/1505.00853/) (2015)
- [Differentially Private Federated Learning: A Client Level Perspective](https://mlsanity.com/paper/1712.07557/) (2017)
- [iDLG: Improved Deep Leakage from Gradients](https://mlsanity.com/paper/2001.02610/) (2020)
- [Salvaging Federated Learning by Local Adaptation](https://mlsanity.com/paper/2002.04758/) (2020)
- [Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective](https://mlsanity.com/paper/2012.06043/) (2020)
- [Selective Aggregation for Low-Rank Adaptation in Federated Learning](https://mlsanity.com/paper/2410.01463/) (2024)
- [A Framework for Evaluating Gradient Leakage Attacks in Federated Learning](https://mlsanity.com/paper/2004.10397/) (2020)
- [CINIC-10 is not ImageNet or CIFAR-10](https://mlsanity.com/paper/1810.03505/) (2018)
- [Gradient Leakage Defense with Key-Lock Module for Federated Learning](https://mlsanity.com/paper/2305.04095/) (2023)
- [Temporal Gradient Inversion Attacks with Robust Optimization](https://mlsanity.com/paper/2306.07883/) (2023)

## Built on by (most cited first, 6 of 6)

- [Exploring the Vulnerabilities of Federated Learning: A Deep Dive into Gradient Inversion Attacks](https://mlsanity.com/paper/2503.11514/) (2025)
- [FedVLMBench: Benchmarking Federated Fine-Tuning of Vision-Language Models](https://mlsanity.com/paper/2506.09638/) (2025)
- [Exploring Federated Pruning for Large Language Models](https://mlsanity.com/paper/2505.13547/) (2025)
- [Multi-Hop Privacy Propagation for Differentially Private Federated Learning in Social Networks](https://mlsanity.com/paper/2508.07676/) (2025)
- [Images in Motion?: A First Look into Video Leakage in Collaborative Deep Learning](https://mlsanity.com/paper/2509.09742/) (2025)
- [Rethinking Generative Reconstruction Attacks against Graph Neural Network Models](https://mlsanity.com/paper/2606.29748/) (2026)

## Abstract (arXiv)

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.