Xiatian Zhu

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.

Federated Learning (FL) has emerged as a powerful paradigm for decentralized model training, yet it remains vulnerable to deep leakage (DL) attacks that reconstruct private client data from shared model updates. While prior DL methods have demonstrated varying levels of success, they often suffer from instability, limited fidelity, or poor robustness under realistic FL settings. We introduce a new DL attack that integrates a generative Flow Matching (FM) prior into the reconstruction process. By guiding optimization toward the distribution of realistic images (represented by a flow matching foundation model), our method enhances reconstruction fidelity without requiring knowledge of the private data. Extensive experiments on multiple datasets and target models demonstrate that our approach consistently outperforms state-of-the-art attacks across pixel-level, perceptual, and feature-based similarity metrics. Crucially, the method remains effective across different training epochs, larger client batch sizes, and under common defenses such as noise injection, clipping, and sparsification. Our findings call for the development of new defense strategies that explicitly account for adversaries equipped with powerful generative priors.