61 citations · 90 across the 13 of their papers we have counts for
3 papers · 1 filter
Provable Privacy Advantages of Decentralized Federated Learning via Distributed Optimization
Wenrui Yu, Qiongxiu Li, Milan Lopuhaä-Zwakenberg +2
Federated learning (FL) emerged as a paradigm designed to improve data privacy by enabling data to reside at its source, thus embedding privacy as a core consideration in FL archit…
Privacy-Preserving Distributed Expectation Maximization for Gaussian Mixture Model using Subspace Perturbation
Qiongxiu Li, Jaron Skovsted Gundersen, Katrine Tjell +2
Privacy has become a major concern in machine learning. In fact, the federated learning is motivated by the privacy concern as it does not allow to transmit the private data but on…
On the Privacy Effect of Data Enhancement via the Lens of Memorization
Xiao Li, Qiongxiu Li, Zhanhao Hu +1
Machine learning poses severe privacy concerns as it has been shown that the learned models can reveal sensitive information about their training data. Many works have investigated…