在这几种制约因素之间的平衡下,作者提出了Power of Choice客户端选择策略 可以描述为πpow−d,中心服务器选择St的方法如下. 1.选择客户端组A(A中一共d个clients). 2.估计本地损失:服务器将目前的全局模型发送给A组的客户端,计算并将本地损失传给服务器 3.选择损失最大的客户端:从A中选取Fk(w)最大的m...
•据我们所知,这是第一次尝试在MEC中调查客户选择优化,以平衡能源消耗之间的权衡和证明了优化问题是np完全的。 •提出了一种能量-精度平衡启发式算法,即基于精度-能量的客户端选择联邦学习(FedAECS),以在多项式时间内逼近最优解。特别是,FedAECS根据学习时间、数据大小和通道质量对边缘客户端进行优先级排序。此外...
然而,这种直接的方法将导致网络带宽的低效使用,并浪费延迟客户端的资源。 FE DCS: FEDERATED LEARNING WITH CLIENT SELECTION 我们提出了一种新的FL协议FedCS,它能有效地处理具有异构资源的客户端。在下面的章节中,我们首先总结了我们提案的几个假设,然后更详细地介绍了联邦存款信用证。 Assumptions 如图1所示,我们认为...
Intelligent Bilateral Client Selection in Federated Learning Using Game Theory. (c2022) 来自 Semantic Scholar 喜欢 0 阅读量: 1 作者: Osama Wehbi 摘要: ederated Learning (FL) is a novel distributed privacy-preserving learning paradigm, which enables the collaboration among several participants (e.g...
Moreover, FedAR also depictsimpressive performance in the presence of a large number of clients with severeclient unavailability.Keywords: Federated learning · Client selection · Bias mitigation1 IntroductionFederated learning (FL) allows multiple clients to collaboratively learn a powerfulglobal machine ...
In this paper, we propose the client evaluationand revision in federated learning (CERFL), which effectively identifies and prevents free-riders, quantifies participants' contributions according to the parameters they upload. We introduce the conceptof a parameter client contribution score (CCS) to ...
Client selection strategies have become a widely adopted approach in recent years within the studies on Federated Learning (FL). This strategy aims to hand... Yazhi Liu,Haonan Xia,Wei Li,... - Peer-to-Peer Networking and Applications 被引量: 0发表: 2025年 Federated learning design and funct...
Federated Learning allows collaborative training without data sharing in settings where participants do not trust the central server and one another. Privacy can be further improved by ensuring that communication between the participants and the server is anonymized through a shuffle; decoupling the part...
在联邦学习中,许多客户端可能会提供类似的(多余的)梯度信息来更新服务器模型,将所有这些更新传输到服务器是一种通信和计算资源的浪费。为此作者提出了DivFL,通过子模最大化来选择携带代表性梯度信息的客户端。在合成和真实的数据集上进行验证。结果表明该方法能提高学习效率,更快的收敛,以及跨客户的更统一(即公平)...
Context-Aware Online Client Selection for Hierarchical Federated Learning 本文主要研究了客户端的选择问题。即每一轮尽可能多地选择客户,并且在每个客户端预算有限的情况下。提出了一种COCS策略,该算法观察客户端本地计算和传输的辅助信息,作出客户端的决策,以在有限预算的情况下最大化网络运营商的效用。