368 citations · 1.5k across the 66 of their papers we have counts for
9 papers · 1 filter
Can You Really Backdoor Federated Learning?
Ziteng Sun, Peter Kairouz, Ananda Theertha Suresh +1
The decentralized nature of federated learning makes detecting and defending against adversarial attacks a challenging task. This paper focuses on backdoor attacks in the federated…
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…
Theoretical Guarantees for Model Auditing with Finite Adversaries
Mario Diaz, Peter Kairouz, Jiachun Liao +1
Privacy concerns have led to the development of privacy-preserving approaches for learning models from sensitive data. Yet, in practice, even models learned with privacy guarantees…
Generative Models for Effective ML on Private, Decentralized Datasets
Sean Augenstein, H. Brendan McMahan, Daniel Ramage +5
To improve real-world applications of machine learning, experienced modelers develop intuition about their datasets, their models, and how the two interact. Manual inspection of ra…
Context-Aware Local Differential Privacy
Jayadev Acharya, Keith Bonawitz, Peter Kairouz +2
Local differential privacy (LDP) is a strong notion of privacy for individual users that often comes at the expense of a significant drop in utility. The classical definition of LD…
Generating Fair Universal Representations using Adversarial Models
Peter Kairouz, Jiachun Liao, Chong Huang +3
We present a data-driven framework for learning fair universal representations (FUR) that guarantee statistical fairness for any learning task that may not be known a priori. Our f…
A Tunable Loss Function for Robust Classification: Calibration, Landscape, and Generalization
Tyler Sypherd, Mario Diaz, John Kevin Cava +3
We introduce a tunable loss function called -loss, parameterized by , which interpolates between the exponential loss (), the log-loss (), and the…
A Tunable Loss Function for Binary Classification
Tyler Sypherd, Mario Diaz, Lalitha Sankar +1
We present -loss, , a tunable loss function for binary classification that bridges log-loss () and - loss (). We prove that -loss has a…
Federated Heavy Hitters Discovery with Differential Privacy
Wennan Zhu, Peter Kairouz, Brendan McMahan +2
The discovery of heavy hitters (most frequent items) in user-generated data streams drives improvements in the app and web ecosystems, but can incur substantial privacy risks if no…