SpooFL: Spoofing Federated Learning

TL;DR

SpooFL introduces a novel federated learning defense that uses synthetic, unrelated data to mislead attackers, effectively preventing data leakage without harming model performance.

Contribution

This paper presents the first spoofing-based defense in federated learning that employs external generative models to deceive attackers with irrelevant synthetic data.

Findings

SpooFL successfully misleads attackers into believing they have recovered true data.

The method prevents meaningful data leakage while maintaining model accuracy.

SpooFL outperforms existing defenses against deep leakage attacks.

Abstract

Traditional defenses against Deep Leakage (DL) attacks in Federated Learning (FL) primarily focus on obfuscation, introducing noise, transformations or encryption to degrade an attacker's ability to reconstruct private data. While effective to some extent, these methods often still leak high-level information such as class distributions or feature representations, and are frequently broken by increasingly powerful denoising attacks. We propose a fundamentally different perspective on FL defense: framing it as a spoofing problem.We introduce SpooFL (Figure 1), a spoofing-based defense that deceives attackers into believing they have recovered the true training data, while actually providing convincing but entirely synthetic samples from an unrelated task. Unlike prior synthetic-data defenses that share classes or distributions with the private data and thus still leak semantic information, SpooFL uses a state-of-the-art generative model trained on an external dataset with no class overlap. As a result, attackers are misled into recovering plausible yet completely irrelevant samples, preventing meaningful data leakage while preserving FL training integrity. We implement the first example of such a spoofing defense, and evaluate our method against state-of-the-art DL defenses and demonstrate that it successfully misdirects attackers without compromising model performance significantly.