具有自利队列稳定性的分层联邦学习激励机制设计

IF 5.4 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Zhuo Li;Fangxing Geng
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引用次数: 0

摘要

集中式人工智能模型训练中潜在的隐私泄露问题引起了公众的极大关注。分层联邦学习作为一种解决隐私和网络效率问题的技术,使用边缘服务器协调本地设备进行模型训练和参数更新,从而减少与中央云服务器的通信,降低隐私泄露的风险。然而,在这种情况下,节点自私自利的兴起提出了一个重大挑战,它破坏了局部模型的训练效率和质量,从而影响了整个系统的性能。本文通过引入虚拟节点自利队列来描述动态自利,同时考虑训练成本和奖励,并提出在受控节点自利范围内最大化模型质量的问题来解决这一问题。利用Lyapunov优化,将该问题分为两个子问题:控制节点数据量和优化节点关联。为了解决这些问题,我们提出了基于匈牙利方法的数据数量控制和客户关联(DCCA)算法。该算法保证了系统的有界性、稳定性和最优性。实验结果表明,与Fmore和fedag算法相比,DCCA算法分别提高了8.43%和13.83%的模型质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Incentive Mechanism Design for Hierarchical Federated Learning With Selfishness Queue Stability
The potential privacy breaches in centralized artificial intelligence model training have raised significant public concern. Hierarchical federated learning, as a technology addressing privacy and network efficiency issues, coordinates local devices using edge servers for model training and parameter updates, thereby reducing communication with central cloud servers and diminishing the risk of privacy leaks. However, in this context, the rise of node selfishness presents a significant challenge, undermining training efficiency and the quality of local models, thereby impacting the overall system’s performance. This paper addresses the issue by introducing a virtual node selfish queue to characterize dynamic selfishness, considering both training costs and rewards, and formulating the problem of maximizing model quality within the bounds of controlled node selfishness. Utilizing Lyapunov optimization, this issue is divided into two subproblems: controlling the quantity of node data and optimizing node associations. To solve these, we propose the Data Quantity Control and Client Association (DCCA) algorithm, based on the Hungarian method. This algorithm is shown to ensure boundedness, stability, and optimality in the system. Experimental results demonstrate that the DCCA algorithm enhances model quality by 8.43% and 13.83% compared to the Fmore and FedAvg algorithms, respectively.
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来源期刊
IEEE Transactions on Emerging Topics in Computing
IEEE Transactions on Emerging Topics in Computing Computer Science-Computer Science (miscellaneous)
CiteScore
12.10
自引率
5.10%
发文量
113
期刊介绍: IEEE Transactions on Emerging Topics in Computing publishes papers on emerging aspects of computer science, computing technology, and computing applications not currently covered by other IEEE Computer Society Transactions. Some examples of emerging topics in computing include: IT for Green, Synthetic and organic computing structures and systems, Advanced analytics, Social/occupational computing, Location-based/client computer systems, Morphic computer design, Electronic game systems, & Health-care IT.
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