# Fingerprinting Deep Learning Models via Network Traffic Patterns in Federated Learning

arXiv: https://arxiv.org/abs/2506.03207 · HTML: https://mlsanity.com/paper/2506.03207/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 25 references that are in the map.
- First posted on arXiv 2 Jun 2025.

## Builds on (most cited first, 7 of 7)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Exploiting Unintended Feature Leakage in Collaborative Learning](https://mlsanity.com/paper/1805.04049/) (2018)
- [Robust Smartphone App Identification Via Encrypted Network Traffic Analysis](https://mlsanity.com/paper/1704.06099/) (2017)
- [Tik-Tok: The Utility of Packet Timing in Website Fingerprinting Attacks](https://mlsanity.com/paper/1902.06421/) (2019)
- [Peek-a-Boo: I see your smart home activities, even encrypted!](https://mlsanity.com/paper/1808.02741/) (2018)
- [Scalable and Robust Mobile Activity Fingerprinting via Over-the-Air Control Channel in 5G Networks](https://mlsanity.com/paper/2409.12572/) (2024)

## Built on by (most cited first, 1 of 1)

- [FLARE: A Wireless Side-Channel Fingerprinting Attack on Federated Learning](https://mlsanity.com/paper/2512.10296/) (2025)

## Abstract (arXiv)

Federated Learning (FL) is increasingly adopted as a decentralized machine learning paradigm due to its capability to preserve data privacy by training models without centralizing user data. However, FL is susceptible to indirect privacy breaches via network traffic analysis-an area not explored in existing research. The primary objective of this research is to study the feasibility of fingerprinting deep learning models deployed within FL environments by analyzing their network-layer traffic information. In this paper, we conduct an experimental evaluation using various deep learning architectures (i.e., CNN, RNN) within a federated learning testbed. We utilize machine learning algorithms, including Support Vector Machines (SVM), Random Forest, and Gradient-Boosting, to fingerprint unique patterns within the traffic data. Our experiments show high fingerprinting accuracy, achieving 100% accuracy using Random Forest and around 95.7% accuracy using SVM and Gradient Boosting classifiers. This analysis suggests that we can identify specific architectures running within the subsection of the network traffic. Hence, if an adversary knows about the underlying DL architecture, they can exploit that information and conduct targeted attacks. These findings suggest a notable security vulnerability in FL systems and the necessity of strengthening it at the network level.