Device Selection and Resource Allocation With Semi-Supervised Method for Federated Edge Learning

IF 7.7 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Ruihan Hu;Haochen Yuan;Daimin Tan;Zhongjie Wang
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引用次数: 0

Abstract

With the rapid growth of distributed learning and workflow orchestration, Federated Edge Learning has emerged as a solution, enabling multiple edge devices to collaboratively train a large model without the need for sharing raw data. Beyond considering bandwidth and computational resource limitations in the Internet of Things (IoT) environment, it is crucial to address the issue of IoT devices often collecting data that lacks timely annotations, which can lead to latency and label deficiency issues. In most Federated Edge Learning mechanisms, clients’ weights are selected for offloading to the server. In this paper, we propose a solution for dynamic edge selection and wireless network allocation under semi-supervised and privacy protection settings, termed Semi-supervised Scheduling and Allocation Optimization for Federated Edge Learning (SSAFL). SSAFL is designed to adapt to various scenarios, including channel state variations, device heterogeneity, resource incentives, deadline control, label deficiencies, and Non-IID data distributions. This adaptability is achieved through the utilization of an Incentive Optimization framework that encompasses bandwidth allocation and device scheduling policies. Within SSAFL, we introduce the concept of a weighted bipartite graph network to tackle the Incentive Optimization problem and achieve a balance in large-scale optimization of device selection. Additionally, to address the label deficiency issue, we devise a Dynamic Timer for deadline control for each client. Comprehensive and confidential results demonstrate that our proposed approach significantly outperforms other Federated Edge Learning baselines.
基于半监督方法的联邦边缘学习设备选择与资源分配
随着分布式学习和工作流编排的快速发展,联邦边缘学习已经成为一种解决方案,使多个边缘设备能够协作训练大型模型,而无需共享原始数据。除了考虑物联网(IoT)环境中的带宽和计算资源限制之外,解决物联网设备经常收集缺乏及时注释的数据的问题至关重要,这可能导致延迟和标签不足问题。在大多数联邦边缘学习机制中,选择客户端的权重来卸载到服务器。在本文中,我们提出了一种在半监督和隐私保护设置下动态边缘选择和无线网络分配的解决方案,称为半监督联邦边缘学习调度和分配优化(SSAFL)。SSAFL旨在适应各种场景,包括通道状态变化、设备异构、资源激励、截止日期控制、标签缺陷和非iid数据分布。这种适应性是通过利用激励优化框架实现的,该框架包括带宽分配和设备调度策略。在SSAFL中,我们引入了加权二部图网络的概念来解决激励优化问题,实现了设备选择大规模优化的平衡。此外,为了解决标签不足的问题,我们设计了一个动态计时器来控制每个客户端的截止日期。全面和保密的结果表明,我们提出的方法显着优于其他联邦边缘学习基线。
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来源期刊
IEEE Transactions on Mobile Computing
IEEE Transactions on Mobile Computing 工程技术-电信学
CiteScore
12.90
自引率
2.50%
发文量
403
审稿时长
6.6 months
期刊介绍: IEEE Transactions on Mobile Computing addresses key technical issues related to various aspects of mobile computing. This includes (a) architectures, (b) support services, (c) algorithm/protocol design and analysis, (d) mobile environments, (e) mobile communication systems, (f) applications, and (g) emerging technologies. Topics of interest span a wide range, covering aspects like mobile networks and hosts, mobility management, multimedia, operating system support, power management, online and mobile environments, security, scalability, reliability, and emerging technologies such as wearable computers, body area networks, and wireless sensor networks. The journal serves as a comprehensive platform for advancements in mobile computing research.
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