208 citations · 448 across the 32 of their papers we have counts for
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cs.LG2020★ 3 cited
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…
cs.LG2020★ 112 cited
IBM Federated Learning: an Enterprise Framework White Paper V0.1
Heiko Ludwig, Nathalie Baracaldo, Gegi Thomas +21
Federated Learning (FL) is an approach to conduct machine learning without centralizing training data in a single place, for reasons of privacy, confidentiality or data volume. How…