# FLARE: A Wireless Side-Channel Fingerprinting Attack on Federated Learning

arXiv: https://arxiv.org/abs/2512.10296 · HTML: https://mlsanity.com/paper/2512.10296/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 39 references that are in the map.
- First posted on arXiv 11 Dec 2025.

## Builds on (most cited first, 15 of 15)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [Multimodal Machine Learning: A Survey and Taxonomy](https://mlsanity.com/paper/1705.09406/) (2017)
- [Robust Aggregation for Federated Learning](https://mlsanity.com/paper/1912.13445/) (2019)
- [Multimodal Deep Learning](https://mlsanity.com/paper/2301.04856/) (2023)
- [In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning](https://mlsanity.com/paper/1809.07857/) (2018)
- [Are Your Sensitive Attributes Private? Novel Model Inversion Attribute Inference Attacks on Classification Models](https://mlsanity.com/paper/2201.09370/) (2022)
- [On Convergence of FedProx: Local Dissimilarity Invariant Bounds, Non-smoothness and Beyond](https://mlsanity.com/paper/2206.05187/) (2022)
- [FedMIA: An Effective Membership Inference Attack Exploiting "All for One" Principle in Federated Learning](https://mlsanity.com/paper/2402.06289/) (2024)
- [Deep Learning Model Inversion Attacks and Defenses: A Comprehensive Survey](https://mlsanity.com/paper/2501.18934/) (2025)
- [Peek-a-Boo: I see your smart home activities, even encrypted!](https://mlsanity.com/paper/1808.02741/) (2018)
- [Browser Fingerprinting: A survey](https://mlsanity.com/paper/1905.01051/) (2019)
- [Physical Layer-Based Device Fingerprinting for Wireless Security: From Theory to Practice](https://mlsanity.com/paper/2506.09807/) (2025)
- [Fingerprinting Deep Learning Models via Network Traffic Patterns in Federated Learning](https://mlsanity.com/paper/2506.03207/) (2025)

## Built on by (most cited first, 2 of 2)

- [Model Forensics in AI-Native Wireless Networks: Taxonomy, Applications, and Case Study](https://mlsanity.com/paper/2605.14387/) (2026)
- [FLINT: Fingerprinting Federated Learning Architectures from 5G PHY-Layer Side Channels](https://mlsanity.com/paper/2607.15469/) (2026)

## Abstract (arXiv)

Federated Learning (FL) enables collaborative model training across distributed devices while safeguarding data and user privacy. However, FL remains susceptible to privacy threats that can compromise data via direct means. That said, indirectly compromising the confidentiality of the FL model architecture (e.g., a convolutional neural network (CNN) or a recurrent neural network (RNN)) on a client device by an outsider remains unexplored. If leaked, this information can enable next-level attacks tailored to the architecture. This paper proposes a novel side-channel fingerprinting attack, leveraging flow-level and packet-level statistics of encrypted wireless traffic from an FL client to infer its deep learning model architecture. We name it FLARE, a fingerprinting framework based on FL Architecture REconnaissance. Evaluation across various CNN and RNN variants-including pre-trained and custom models trained over IEEE 802.11 Wi-Fi-shows that FLARE achieves over 98% F1-score in closed-world and up to 91% in open-world scenarios. These results reveal that CNN and RNN models leak distinguishable traffic patterns, enabling architecture fingerprinting even under realistic FL settings with hardware, software, and data heterogeneity. To our knowledge, this is the first work to fingerprint FL model architectures by sniffing encrypted wireless traffic, exposing a critical side-channel vulnerability in current FL systems.