基于自适应校准策略的轻量级个性化联邦学习

IF 5.8 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Dongshang Deng;Xuangou Wu;Tao Zhang;Chaocan Xiang;Wei Zhao;Minrui Xu;Jiawen Kang;Zhu Han;Dusit Niyato
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引用次数: 0

摘要

联邦学习(FL)是一个很有前途的人工智能框架,它使客户能够集体训练具有数据隐私的模型。然而,在现实世界中,为了构建实用的FL框架,必须解决几个挑战,包括统计异质性、受限资源和公平性。因此,我们首先研究了局部模型初始化过程中由统计异质性引起的聚集差距,这不仅会给客户端带来额外的计算开销,还会导致公平性的降低。为了弥补这一差距,我们提出了pFedCal,一种新颖的个性化联邦学习,具有轻量级自适应校准策略,通过客户端的先验知识进行校准补偿。具体来说,我们在模型初始化时为每个客户端引入补偿,补偿来自全局梯度和最新梯度偏差。为了提高标定效果,引入了基于平滑的标定策略,并设计了自适应标定策略。一个典型的例子表明,所提出的校准和平滑策略提高了客户的公平性。理论分析表明,在适当的学习率下,pFedCal收敛于非凸损失函数的一阶平稳点。综合实验结果表明,与现有方法相比,pFedCal具有更快的收敛速度、更高的精度和更好的公平性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
pFedCal: Lightweight Personalized Federated Learning With Adaptive Calibration Strategy
Federated learning (FL) is a promising artificial intelligence framework that enables clients to collectively train models with data privacy. However, in real-world scenarios, to construct practical FL frameworks, several challenges have to be addressed, including statistical heterogeneity, constrained resources, and fairness. Therefore, we first investigate an aggregation gap caused by statistical heterogeneity during local model initialization, which not only causes additional computational overhead for clients but also leads to the degradation of fairness. To bridge this gap, we propose pFedCal, a novel personalized federated learning with lightweight adaptive calibration strategy that performs calibration compensation through the prior knowledge of clients. Specifically, we introduce compensation for each client at the model initialization, with the compensation derived from the global gradient and the latest gradient bias. To enhance the calibration effect, we introduce a smoothing-based calibration strategy, and we design an adaptive calibration strategy. A representative example demonstrates that the proposed calibration and smoothing strategies improve fairness for clients. The theoretical analysis indicates that with an appropriate learning rate, pFedCal converges to a first-order stationary point for non-convex loss functions. Comprehensive experimental results show that pFedCal achieves faster convergence, higher accuracy, and improved fairness than the state-of-the-art methods.
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来源期刊
IEEE Transactions on Services Computing
IEEE Transactions on Services Computing COMPUTER SCIENCE, INFORMATION SYSTEMS-COMPUTER SCIENCE, SOFTWARE ENGINEERING
CiteScore
11.50
自引率
6.20%
发文量
278
审稿时长
>12 weeks
期刊介绍: IEEE Transactions on Services Computing encompasses the computing and software aspects of the science and technology of services innovation research and development. It places emphasis on algorithmic, mathematical, statistical, and computational methods central to services computing. Topics covered include Service Oriented Architecture, Web Services, Business Process Integration, Solution Performance Management, and Services Operations and Management. The transactions address mathematical foundations, security, privacy, agreement, contract, discovery, negotiation, collaboration, and quality of service for web services. It also covers areas like composite web service creation, business and scientific applications, standards, utility models, business process modeling, integration, collaboration, and more in the realm of Services Computing.
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