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Found 7 papers, 0 papers with code
SoK: Verifiable Cross-Silo FL
Federated Learning (FL) is a widespread approach that allows training machine learning (ML) models with data distributed across multiple devices.
DP-SGD with weight clipping
Recently, due to the popularity of deep neural networks and other methods whose training typically relies on the optimization of an objective function, and due to concerns for data privacy, there is a lot of interest in differentially private gradient descent methods.
An Accurate, Scalable and Verifiable Protocol for Federated Differentially Private Averaging
Learning from data owned by several parties, as in federated learning, raises challenges regarding the privacy guarantees provided to participants and the correctness of the computation in the presence of malicious parties.
Hiding in the Crowd: A Massively Distributed Algorithm for Private Averaging with Malicious Adversaries
The amount of personal data collected in our everyday interactions with connected devices offers great opportunities for innovative services fueled by machine learning, as well as raises serious concerns for the privacy of individuals.
Learning from networked examples
Many machine learning algorithms are based on the assumption that training examples are drawn independently.
Predicate Logic as a Modeling Language: Modeling and Solving some Machine Learning and Data Mining Problems with IDP3
This paper provides a gentle introduction to problem solving with the IDP3 system.
Learning from networked examples in a k-partite graph
Many machine learning algorithms are based on the assumption that training examples are drawn independently.
