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Found 6 papers, 1 papers with code
Federated Learning with Autotuned Communication-Efficient Secure Aggregation
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 machine learning from the need to store the data in the cloud.
Context-Aware Local Differential Privacy
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.
Towards Federated Learning at Scale: System Design
Federated Learning is a distributed machine learning approach which enables model training on a large corpus of decentralized data.
Practical Secure Aggregation for Federated Learning on User-Held Data
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 values themselves.
Discrete Distribution Estimation under Local Privacy
The collection and analysis of user data drives improvements in the app and web ecosystems, but comes with risks to privacy.
Church: a language for generative models
We introduce Church, a universal language for describing stochastic generative processes.
