IF 6.7 2区 计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY IEEE Transactions on Network Science and Engineering Pub Date : 2024-11-28 DOI:10.1109/TNSE.2024.3508594
Yulan Gao;Chao Ren;Han Yu;Ming Xiao;Mikael Skoglund
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摘要

在快速发展的联合学习(FL)领域,确保高效的联合学习任务委托,同时激励联合学习客户的参与,是一项重大挑战,尤其是在联合学习参与者覆盖范围有限的无线网络中。现有的基于契约理论的方法是在系统中只有一个 FL 服务器的假设(即垄断市场假设)下设计的,这在实践中是不现实的。为解决这一局限性,我们提出了公平感知多服务器 FL 任务委托方法(FAMuS),这是一种基于契约理论和 Lyapunov 优化的新型框架,可共同解决无线多服务器 FL 网络(WMSFLN)面临的这些复杂问题。在给定的 WMSFLN 中,任务请求者会生成多个 FL 任务,并将其委托给 FL 服务器,由 FL 服务器协调训练过程。为确保公平对待 FL 服务器,FAMuS 建立了虚拟队列来跟踪它们之前对 FL 任务的访问情况,并根据结果更新 FL 模型性能。其目标是最大限度地降低 WMSFLN 中的时间平均成本,同时确保所有队列保持稳定。鉴于有关 FL 客户参与成本的信息不完整,以及 WMSFLN 状态的不可预测性(取决于移动客户端的位置),这一点尤其具有挑战性。在两个真实数据集的基础上,FAMuS 与五种最先进的方法进行了广泛的实验比较,结果表明,与表现最好的基线方法相比,FAMuS 的测试准确率平均提高了 6.91%,成本降低了 27.34%,公平性提高了 0.63%。
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Fairness-Aware Multi-Server Federated Learning Task Delegation Over Wireless Networks
In the rapidly advancing field of federated learning (FL), ensuring efficient FL task delegation while incentivizing FL client participation poses significant challenges, especially in wireless networks where FL participants' coverage is limited. Existing Contract Theory-based methods are designed under the assumption that there is only one FL server in the system (i.e., the monopoly market assumption), which in unrealistic in practice. To address this limitation, we propose Fairness-Aware Multi-Server FL task delegation approach (FAMuS), a novel framework based on Contract Theory and Lyapunov optimization to jointly address these intricate issues facing wireless multi-server FL networks (WMSFLN). Within a given WMSFLN, a task requester products multiple FL tasks and delegate them to FL servers which coordinate the training processes. To ensure fair treatment of FL servers, FAMuS establishes virtual queues to track their previous access to FL tasks, updating them in relation to the resulting FL model performance. The objective is to minimize the time-averaged cost in a WMSFLN, while ensuring all queues remain stable. This is particularly challenging given the incomplete information regarding FL clients' participation cost and the unpredictable nature of the WMSFLN state, which depends on the locations of the mobile clients. Extensive experiments comparing FAMuS against five state-of-the-art approaches based on two real-world datasets demonstrate that it achieves 6.91% higher test accuracy, 27.34% lower cost, and 0.63% higher fairness on average than the best-performing baseline.
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来源期刊
IEEE Transactions on Network Science and Engineering
IEEE Transactions on Network Science and Engineering Engineering-Control and Systems Engineering
CiteScore
12.60
自引率
9.10%
发文量
393
期刊介绍: The proposed journal, called the IEEE Transactions on Network Science and Engineering (TNSE), is committed to timely publishing of peer-reviewed technical articles that deal with the theory and applications of network science and the interconnections among the elements in a system that form a network. In particular, the IEEE Transactions on Network Science and Engineering publishes articles on understanding, prediction, and control of structures and behaviors of networks at the fundamental level. The types of networks covered include physical or engineered networks, information networks, biological networks, semantic networks, economic networks, social networks, and ecological networks. Aimed at discovering common principles that govern network structures, network functionalities and behaviors of networks, the journal seeks articles on understanding, prediction, and control of structures and behaviors of networks. Another trans-disciplinary focus of the IEEE Transactions on Network Science and Engineering is the interactions between and co-evolution of different genres of networks.
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Table of Contents Degradation Estimation for Distributed Nonlinear Systems: A PDF-Consensus Particle Filtering Method A Hybrid Semi-Asynchronous Federated Learning and Split Learning Strategy in Edge Networks A Hybrid Multi-Agent System Approach for Distributed Composite Convex Optimization Under Unbalanced Directed Graphs Weighted Average Consensus Algorithms in Distributed and Federated Learning
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