5 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…
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 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…
Adaptive Differentially Quantized Subspace Perturbation (ADQSP): A Unified Framework for Privacy-Preserving Distributed Average Consensus
Qiongxiu Li, Jaron Skovsted Gundersen, Milan Lopuhaa-Zwakenberg +1
Privacy-preserving distributed average consensus has received significant attention recently due to its wide applicability. Based on the achieved performances, existing approaches…