61 citations · 245 across the 30 of their papers we have counts for
5 papers · 1 filter
Re-Evaluating Privacy in Centralized and Decentralized Learning: An Information-Theoretical and Empirical Study
Changlong Ji, Stephane Maag, Richard Heusdens +1
Decentralized Federated Learning (DFL) has garnered attention for its robustness and scalability compared to Centralized Federated Learning (CFL). While DFL is commonly believed to…
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…
Wideband Relative Transfer Function (RTF) Estimation Exploiting Frequency Correlations
Giovanni Bologni, Richard C. Hendriks, Richard Heusdens
This article focuses on estimating relative transfer functions (RTFs) for beamforming applications. Traditional methods often assume that spectra are uncorrelated, an assumption th…
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…
Optimal Pilot Design for OTFS in Linear Time-Varying Channels
Ids van der Werf, Richard Heusdens, Richard C. Hendriks +1
This paper investigates the positioning of the pilot symbols, as well as the power distribution between the pilot and the communication symbols in the orthogonal time frequency spa…