一种新的联邦学习预测技术

IF 5.1 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Cláudio G. S. Capanema;Allan M. de Souza;Joahannes B. D. da Costa;Fabrício A. Silva;Leandro A. Villas;Antonio A. F. Loureiro
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引用次数: 0

摘要

研究人员已经研究了如何在统计和系统异质性、通信成本和隐私等各个领域改进联邦学习(FL)。到目前为止,大多数建议的解决方案要么与应用程序上下文密切相关,要么复杂到无法在涉及人类的实际应用程序中广泛复制。开发可被绝大多数FL结构所利用并且独立于人们使用的应用程序的模块化解决方案是本文开辟的新的研究方向。在这项工作中,我们提出了一个插件(名为FedPredict)来同时解决三个问题:数据异构,新/未经培训和/或过时客户端的低性能,以及通信成本。我们主要通过在推理步骤中结合全局和局部参数(这带来了泛化和个性化)来实现这一目标,同时采用层选择和矩阵分解技术来降低下行通信成本(服务器到客户端)。由于其简单性,它可以应用于不同数量拓扑的联合学习。结果表明,将所提出的插件添加到给定的FL解决方案中,与原始解决方案相比,可以显着降低高达83.3%的下行通信成本,并提高高达304%的精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Novel Prediction Technique for Federated Learning
Researchers have studied how to improve Federated Learning (FL) in various areas, such as statistical and system heterogeneity, communication cost, and privacy. So far, most of the proposed solutions are either very tied to the application context or complex to be broadly reproduced in real-life applications involving humans. Developing modular solutions that can be leveraged by the vast majority of FL structures and are independent of the application people use is the new research direction opened by this paper. In this work, we propose a plugin (named FedPredict) to address three problems simultaneously: data heterogeneity, low performance of new/untrained and/or outdated clients, and communication cost. We do so mainly by combining global and local parameters (which brings generalization and personalization) in the inference step while adapting layer selection and matrix factorization techniques to reduce the downlink communication cost (server to client). Due to its simplicity, it can be applied to federated learning of different number of topologies. Results show that adding the proposed plugin to a given FL solution can significantly reduce the downlink communication cost by up to 83.3% and improve accuracy by up to 304% compared to the original solution.
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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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