Fingerprinting Deep Learning Models via Network Traffic Patterns in Federated Learning

TL;DR

This paper demonstrates that deep learning models in federated learning can be accurately fingerprinted through network traffic analysis, revealing a significant security vulnerability that could enable targeted attacks.

Contribution

It introduces a novel method for fingerprinting DL models in FL environments by analyzing network traffic patterns, highlighting a new privacy risk.

Findings

High fingerprinting accuracy with Random Forest (100%)

Effective identification of CNN and RNN architectures

Reveals security vulnerabilities in federated learning systems

Abstract

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.