438 citations · 670 across the 13 of their papers we have counts for
4 papers · 1 filter
Advances and Open Problems in Federated Learning
Peter Kairouz, H. Brendan McMahan, Brendan Avent +56
Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a cen…
SquirRL: Automating Attack Analysis on Blockchain Incentive Mechanisms with Deep Reinforcement Learning
Charlie Hou, Mingxun Zhou, Yan Ji +4
Incentive mechanisms are central to the functionality of permissionless blockchains: they incentivize participants to run and secure the underlying consensus protocol. Designing in…
Adversarial Training and Robustness for Multiple Perturbations
Florian Tramèr, Dan Boneh
Defenses against adversarial examples, such as adversarial training, are typically tailored to a single perturbation type (e.g., small -noise). For other perturbations…
Exploiting Excessive Invariance caused by Norm-Bounded Adversarial Robustness
Jörn-Henrik Jacobsen, Jens Behrmannn, Nicholas Carlini +2
Adversarial examples are malicious inputs crafted to cause a model to misclassify them. Their most common instantiation, "perturbation-based" adversarial examples introduce changes…