Publications (6)
Practical Secure Aggregation for Federated Learning on User-Held Data
Keith Bonawitz, Vladimir Ivanov, Ben Kreuter +6
Secure Aggregation protocols allow a collection of mutually distrust parties, each holding a private value, to collaboratively compute the sum of those values without revealing the…
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…
Federated Learning with Autotuned Communication-Efficient Secure Aggregation
Keith Bonawitz, Fariborz Salehi, Jakub KoneÄný +2
Federated Learning enables mobile devices to collaboratively learn a shared inference model while keeping all the training data on a user's device, decoupling the ability to do mac…
Towards Federated Learning at Scale: System Design
Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp +11
Federated Learning is a distributed machine learning approach which enables model training on a large corpus of decentralized data. We have built a scalable production system for F…
Church: a language for generative models
Noah Goodman, Vikash Mansinghka, Daniel M. Roy +2
We introduce Church, a universal language for describing stochastic generative processes. Church is based on the Lisp model of lambda calculus, containing a pure Lisp as its determ…
Discrete Distribution Estimation under Local Privacy
Peter Kairouz, Keith Bonawitz, Daniel Ramage
The collection and analysis of user data drives improvements in the app and web ecosystems, but comes with risks to privacy. This paper examines discrete distribution estimation un…