Zobrazeno 1 - 10
of 223
pro vyhledávání: '"Heusdens, Richard"'
Decentralized Federated Learning (DFL) has garnered attention for its robustness and scalability compared to Centralized Federated Learning (CFL). While DFL is commonly believed to offer privacy advantages due to the decentralized control of sensitiv
Externí odkaz:
http://arxiv.org/abs/2409.14261
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 of the data involved makes privacy protection essential. Despite its im
Externí odkaz:
http://arxiv.org/abs/2409.10226
Autor:
Zhang, Guoqiang, Heusdens, Richard
In this paper, we extend the standard Attention in transformer by exploiting the consensus discrepancy from a distributed optimization perspective, referred to as AttentionX. It is noted that the primal-dual method of multipliers (PDMM) \cite{Zhang16
Externí odkaz:
http://arxiv.org/abs/2409.04275
This article focuses on estimating relative transfer functions (RTFs) for beamforming applications. While traditional methods assume that spectra are uncorrelated, this assumption is often violated in practical scenarios due to natural phenomena such
Externí odkaz:
http://arxiv.org/abs/2407.14152
Autor:
Yu, Wenrui, Li, Qiongxiu, Lopuhaä-Zwakenberg, Milan, Christensen, Mads Græsbøll, Heusdens, Richard
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 architectures, whether centralized or decentralized. Contrasting with recent
Externí odkaz:
http://arxiv.org/abs/2407.09324
Autor:
Heusdens, Richard, Zhang, Guoqiang
In this paper we consider the problem of distributed nonlinear optimisation of a separable convex cost function over a graph subject to cone constraints. We show how to generalise, using convex analysis, monotone operator theory and fixed-point theor
Externí odkaz:
http://arxiv.org/abs/2405.09490
This paper investigates the positioning of the pilot symbols, as well as the power distribution between the pilot and the communication symbols in the OTFS modulation scheme. We analyze the pilot placements that minimize the mean squared error (MSE)
Externí odkaz:
http://arxiv.org/abs/2403.19379
Privacy-preserving distributed processing has received considerable attention recently. The main purpose of these algorithms is to solve certain signal processing tasks over a network in a decentralised fashion without revealing private/secret data t
Externí odkaz:
http://arxiv.org/abs/2312.08144
Decentralized Federated Learning (FL) has attracted significant attention due to its enhanced robustness and scalability compared to its centralized counterpart. It pivots on peer-to-peer communication rather than depending on a central server for mo
Externí odkaz:
http://arxiv.org/abs/2312.07956
Privacy-preserving distributed average consensus has received significant attention recently due to its wide applicability. Based on the achieved performances, existing approaches can be broadly classified into perfect accuracy-prioritized approaches
Externí odkaz:
http://arxiv.org/abs/2312.07947