# On the Efficiency of Privacy Attacks in Federated Learning

arXiv: https://arxiv.org/abs/2404.09430 · HTML: https://mlsanity.com/paper/2404.09430/

## Key facts

- Cited by 3 papers in the map (data through 7 Oct 2026).
- Builds on 29 of its 45 references that are in the map.
- First posted on arXiv 15 Apr 2024.

## Builds on (most cited first, 29 of 29)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Adaptive Federated Optimization](https://mlsanity.com/paper/2003.00295/) (2020)
- [Deep Leakage from Gradients](https://mlsanity.com/paper/1906.08935/) (2019)
- [How To Backdoor Federated Learning](https://mlsanity.com/paper/1807.00459/) (2018)
- [Inverting Gradients -- How easy is it to break privacy in federated learning?](https://mlsanity.com/paper/2003.14053/) (2020)
- [Federated Learning for Mobile Keyboard Prediction](https://mlsanity.com/paper/1811.03604/) (2018)
- [Federated Learning with Matched Averaging](https://mlsanity.com/paper/2002.06440/) (2020)
- [Local Model Poisoning Attacks to Byzantine-Robust Federated Learning](https://mlsanity.com/paper/1911.11815/) (2019)
- [iDLG: Improved Deep Leakage from Gradients](https://mlsanity.com/paper/2001.02610/) (2020)
- [See through Gradients: Image Batch Recovery via GradInversion](https://mlsanity.com/paper/2104.07586/) (2021)
- [Robust Aggregation for Federated Learning](https://mlsanity.com/paper/1912.13445/) (2019)
- [Horovod: fast and easy distributed deep learning in TensorFlow](https://mlsanity.com/paper/1802.05799/) (2018)
- [Data Poisoning Attacks Against Federated Learning Systems](https://mlsanity.com/paper/2007.08432/) (2020)
- [R-GAP: Recursive Gradient Attack on Privacy](https://mlsanity.com/paper/2010.07733/) (2020)
- [Federated Learning: Opportunities and Challenges](https://mlsanity.com/paper/2101.05428/) (2021)
- [CAFE: Catastrophic Data Leakage in Vertical Federated Learning](https://mlsanity.com/paper/2110.15122/) (2021)
- [SAPAG: A Self-Adaptive Privacy Attack From Gradients](https://mlsanity.com/paper/2009.06228/) (2020)
- [Get a Model! Model Hijacking Attack Against Machine Learning Models](https://mlsanity.com/paper/2111.04394/) (2021)
- [Securing Distributed SGD against Gradient Leakage Threats](https://mlsanity.com/paper/2305.06473/) (2023)
- [Selecting and Composing Learning Rate Policies for Deep Neural Networks](https://mlsanity.com/paper/2210.12936/) (2022)
- [A Comparative Measurement Study of Deep Learning as a Service Framework](https://mlsanity.com/paper/1810.12210/) (2018)
- [Privacy Risks Analysis and Mitigation in Federated Learning for Medical Images](https://mlsanity.com/paper/2311.06643/) (2023)
- [Parallel Detection for Efficient Video Analytics at the Edge](https://mlsanity.com/paper/2107.12563/) (2021)
- [STDLens: Model Hijacking-Resilient Federated Learning for Object Detection](https://mlsanity.com/paper/2303.11511/) (2023)
- [DeepSpark: A Spark-Based Distributed Deep Learning Framework for Commodity Clusters](https://mlsanity.com/paper/1602.08191/) (2016)

## Built on by (most cited first, 3 of 3)

- [Geminio: Language-Guided Gradient Inversion Attacks in Federated Learning](https://mlsanity.com/paper/2411.14937/) (2024)
- [On-device Federated Learning in Smartphones for Detecting Depression from Reddit Posts](https://mlsanity.com/paper/2410.13709/) (2024)
- [STAR-FL: Secure Federated Learning with Spatial-Temporal Analysis and Robust Aggregation](https://mlsanity.com/paper/2608.14861/) (2026)

## Abstract (arXiv)

Recent studies have revealed severe privacy risks in federated learning, represented by Gradient Leakage Attacks. However, existing studies mainly aim at increasing the privacy attack success rate and overlook the high computation costs for recovering private data, making the privacy attack impractical in real applications. In this study, we examine privacy attacks from the perspective of efficiency and propose a framework for improving the Efficiency of Privacy Attacks in Federated Learning (EPAFL). We make three novel contributions. First, we systematically evaluate the computational costs for representative privacy attacks in federated learning, which exhibits a high potential to optimize efficiency. Second, we propose three early-stopping techniques to effectively reduce the computational costs of these privacy attacks. Third, we perform experiments on benchmark datasets and show that our proposed method can significantly reduce computational costs and maintain comparable attack success rates for state-of-the-art privacy attacks in federated learning. We provide the codes on GitHub at https://github.com/mlsysx/EPAFL.