arXiv AI
Privacy-Preserving Personalized Federated Learning (PPP-FL) for Medical Imaging
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arXiv AI
Oct 1
Aegis: Generative Gradient Masking for Privacy-Preserving Medical Federated Learning
Aegis is a client‑side defense for medical federated learning that protects against model inversion attacks by adding a masking gradient derived from locally synthesized data. The method exploits the fact that attacks fail when the effective batch size exceeds the model’s leakage capacity, turning this bottleneck into a privacy guarantee. Experiments on MNIST, CIFAR‑10, and MedMNIST datasets show that Aegis neutralizes state‑of‑the‑art attacks while preserving model accuracy and adding only modest overhead.
arXiv Machine Learning
Jul 15
Reducing information dependency does not cause training data privacy. Adversarially non-robust features do
arXiv:2607. 12354v1 Announce Type: new Abstract: In this paper, we challenge the prevailing view that information dependency (including rote memorization) drives training data exposure to image reconstruction attacks.
Hugging Face Trending Papers
Jul 8
Collaborative Synthetic Data Generation for Knowledge Transfer in Federated Learning
One-shot federated learning (OSFL) addresses the communication overhead of federated learning by limiting training to a single round, but doing so without sacrificing model quality is non-trivial, particularly when client data distributions diverge. Recent work has addressed this challenge by aggregating client knowledge on the server through the construction of transferable synthetic datasets or distillates.
arXiv AI
Jul 9
Collaborative Synthetic Data Generation for Knowledge Transfer in Federated Learning
arXiv:2607. 07565v1 Announce Type: cross Abstract: One-shot federated learning (OSFL) addresses the communication overhead of federated learning by limiting training to a single round, but doing so without sacrificing model quality is non-trivial, particularly when client data distributions diverge.
arXiv Machine Learning
Jun 2
Profiling Privacy Preservation Against Gradient Inversion Attacks in Tabular Federated Learning
arXiv:2606. 00986v1 Announce Type: new Abstract: Federated learning (FL) enables multiple data holders to train machine learning models collaboratively without centralizing raw data, making it useful in privacy sensitive domains such as healthcare and institutional data sharing.
arXiv Machine Learning
Oct 7
Adaptive Model Inversion Attacks Generalize a Privacy-Robustness Tradeoff
arXiv:2610.07677v1 Announce Type: new Abstract: In this paper, we show that standard evaluations of high-resolution Model Inversion Attacks (MIAs) significantly underestimate training-data privacy le...