通过个性化联合学习为跨数据孤岛的时空流动建模

IF 7.7 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS IEEE Transactions on Mobile Computing Pub Date : 2024-09-03 DOI:10.1109/TMC.2024.3453657
Yudong Zhang;Xu Wang;Pengkun Wang;Binwu Wang;Zhengyang Zhou;Yang Wang
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引用次数: 0

摘要

时空移动建模在移动计算的发展中起着举足轻重的作用。如今,数据经常被各种分布式孤岛所掌握,这些孤岛相互隔离,数据共享受到限制。有鉴于此,人们开始尝试在时空移动建模中引入联合学习。同时,时空数据固有的分布异质性也对模型个性化提出了要求。然而,现有的方法都是以模型为中心来解决个性化问题,未能探索各种数据孤岛中的数据特征,从而忽视了模型个性化不足的根本原因是数据的异构分布。在本文中,我们为跨孤岛时空移动建模提出了一个新颖的面向分布的个性化联邦学习框架(名为 FedCroST),该框架利用可学习的时空提示来隐式地表示数据孤岛的本地数据分布模式,并引导本地模型学习个性化信息。具体来说,我们关注时间分布的潜在特征,并设计了一个条件扩散模块来生成时间提示,为时间序列的演变提供指导。同时,我们强调节点邻域固有的结构分布,并提出自适应空间结构分区来构建空间提示,从而增强空间信息表示。此外,我们还引入了去噪自动编码器,以有效利用学习到的多视角时空特征,获得适应本地任务的个性化表征。我们的建议强调了潜在时空数据分布在实现个性化联合时空学习中的重要性,为数据孤岛场景中的时空移动建模提供了新的见解。在真实世界数据集上进行的大量实验表明,在各种跨孤岛时空移动建模任务中,FedCroST 的表现远远优于先进的基线。
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Modeling Spatio-Temporal Mobility Across Data Silos via Personalized Federated Learning
Spatio-temporal mobility modeling plays a pivotal role in the advancement of mobile computing. Nowadays, data is frequently held by various distributed silos, which are isolated from each other and confront limitations on data sharing. Given this, there have been some attempts to introduce federated learning into spatio-temporal mobility modeling. Meanwhile, the distributional heterogeneity inherent in the spatio-temporal data also puts forward requirements for model personalization. However, the existing methods tackle personalization in a model-centric manner and fail to explore the data characteristics in various data silos, thus ignoring the fact that the fundamental cause of insufficient personalization in the model is the heterogeneous distribution of data. In this paper, we propose a novel distribution-oriented personalized Fed erated learning framework for Cro ss-silo S patio- T emporal mobility modeling (named FedCroST ), that leverages learnable spatio-temporal prompts to implicitly represent the local data distribution patterns of data silos and guide the local models to learn the personalized information. Specifically, we focus on the potential characteristics within temporal distribution and devise a conditional diffusion module to generate temporal prompts that serve as guidance for the evolution of the time series. Simultaneously, we emphasize the structure distribution inherent in node neighborhoods and propose adaptive spatial structure partition to construct the spatial prompts, augmenting the spatial information representation. Furthermore, we introduce a denoising autoencoder to effectively harness the learned multi-view spatio-temporal features and obtain personalized representations adapted to local tasks. Our proposal highlights the significance of latent spatio-temporal data distributions in enabling personalized federated spatio-temporal learning, providing new insights into modeling spatio-temporal mobility in data silo scenarios. Extensive experiments conducted on real-world datasets demonstrate that FedCroST outperforms the advanced baselines by a large margin in diverse cross-silo spatio-temporal mobility modeling tasks.
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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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