Search Results for author: Jan Ramon

Found 5 papers, 0 papers with code

An Accurate, Scalable and Verifiable Protocol for Federated Differentially Private Averaging

no code implementations12 Jun 2020 César Sabater, Aurélien Bellet, Jan Ramon

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.

Federated Learning

Hiding in the Crowd: A Massively Distributed Algorithm for Private Averaging with Malicious Adversaries

no code implementations27 Mar 2018 Pierre Dellenbach, Aurélien Bellet, Jan Ramon

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

no code implementations11 May 2014 Yuyi Wang, Jan Ramon, Zheng-Chu Guo

Many machine learning algorithms are based on the assumption that training examples are drawn independently.

Learning from networked examples in a k-partite graph

no code implementations3 Jun 2013 Yuyi Wang, Jan Ramon, Zheng-Chu Guo

Many machine learning algorithms are based on the assumption that training examples are drawn independently.

BIG-bench Machine Learning

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