3 papers
cs.CR2026
SpooFL: Spoofing Federated Learning
Isaac Baglin, Xiatian Zhu, Simon Hadfield
Traditional defenses against Deep Leakage (DL) attacks in Federated Learning (FL) primarily focus on obfuscation, introducing noise, transformations or encryption to degrade an att…
cs.CV2026
Deep Leakage with Generative Flow Matching Denoiser
Isaac Baglin, Xiatian Zhu, Simon Hadfield
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…
cs.CR2025
FEDLAD: Federated Evaluation of Deep Leakage Attacks and Defenses
Isaac Baglin, Xiatian Zhu, Simon Hadfield
Federated Learning is a privacy preserving decentralized machine learning paradigm designed to collaboratively train models across multiple clients by exchanging gradients to the s…