112 citations · 320 across the 20 of their papers we have counts for
20 papers
Towards an Accountable and Reproducible Federated Learning: A FactSheets Approach
Nathalie Baracaldo, Ali Anwar, Mark Purcell +8
Federated Learning (FL) is a novel paradigm for the shared training of models based on decentralized and private data. With respect to ethical guidelines, FL is promising regarding…
Certified Federated Adversarial Training
Giulio Zizzo, Ambrish Rawat, Mathieu Sinn +2
In federated learning (FL), robust aggregation schemes have been developed to protect against malicious clients. Many robust aggregation schemes rely on certain numbers of benign c…
Automated Robustness with Adversarial Training as a Post-Processing Step
Ambrish Rawat, Mathieu Sinn, Beat Buesser
Adversarial training is a computationally expensive task and hence searching for neural network architectures with robustness as the criterion can be challenging. As a step towards…
DLPFS: The Data Leakage Prevention FileSystem
Stefano Braghin, Marco Simioni, Mathieu Sinn
Shared folders are still a common practice for granting third parties access to data files, regardless of the advances in data sharing technologies. Services like Google Drive, Dro…
The Devil is in the GAN: Backdoor Attacks and Defenses in Deep Generative Models
Ambrish Rawat, Killian Levacher, Mathieu Sinn
Deep Generative Models (DGMs) are a popular class of deep learning models which find widespread use because of their ability to synthesize data from complex, high-dimensional manif…
FAT: Federated Adversarial Training
Giulio Zizzo, Ambrish Rawat, Mathieu Sinn +1
Federated learning (FL) is one of the most important paradigms addressing privacy and data governance issues in machine learning (ML). Adversarial training has emerged, so far, as…