10 citations · 12 across the 5 of their papers we have counts for
7 papers
When Is Distributed Nonlinear Aggregation Private? Optimality and Information-Theoretical Bounds
Wenrui Yu, Jaron Skovsted Gundersen, Richard Heusdens +1
Nonlinear aggregation is central to modern distributed systems, yet its privacy behavior is far less understood than that of linear aggregation. Unlike linear aggregation where mat…
Optimal Privacy-Preserving Distributed Median Consensus
Wenrui Yu, Qiongxiu Li, Richard Heusdens +1
Distributed median consensus has emerged as a critical paradigm in multi-agent systems due to the inherent robustness of the median against outliers and anomalies in measurement. D…
Privacy-Preserving Distributed Maximum Consensus Without Accuracy Loss
Wenrui Yu, Richard Heusdens, Jun Pang +1
In distributed networks, calculating the maximum element is a fundamental task in data analysis, known as the distributed maximum consensus problem. However, the sensitive nature o…
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…
Communication efficient privacy-preserving distributed optimization using adaptive differential quantization
Qiongxiu Li, Richard Heusdens, Mads Græsbøll Christensen
Privacy issues and communication cost are both major concerns in distributed optimization. There is often a trade-off between them because the encryption methods required for priva…
Privacy-Preserving Distributed Processing: Metrics, Bounds, and Algorithms
Qiongxiu Li, Jaron Skovsted Gundersen, Richard Heusdens +1
Privacy-preserving distributed processing has recently attracted considerable attention. It aims to design solutions for conducting signal processing tasks over networks in a decen…