HSTFL：一种用于未对齐时空预测的异构联邦学习框架
HSTFL: A Heterogeneous Federated Learning Framework for Misaligned Spatiotemporal Forecasting
AI总结：
针对跨领域时空预测中的隐私保护与数据异构挑战，提出HSTFL框架，通过纵向联邦表示学习与跨客户端虚拟节点对齐模块实现多方协作，在保护隐私的同时显著提升预测性能。
摘要：
时空预测已成为智能交通和智能能源管理等各类智慧城市应用不可或缺的基础组件。最新进展表明，通过整合来自不同领域的地理分布式时间序列数据中的知识，可以显著提升时空预测的性能，例如利用人类移动数据增强房地产评估，以及联合预测出租车和自行车需求。尽管这些方法有效，但现有方法假设存在集中式的数据收集与利用环境，忽视了与不同方拥有的数据相关的隐私和商业利益问题。在本文中，我们研究在无法直接访问多源私有数据的情况下的多方协作时空预测。然而，由于存在1）跨领域特征异构性和2）跨客户端地理异构性，标准的横向或纵向联邦学习无法适用。为此，我们提出了一种异构时空联邦学习（HSTFL）框架，使多个客户端能够在保护隐私的同时，协作利用来自不同领域的地理分布式时间序列数据。具体而言，我们首先设计了纵向联邦时空表示学习，以在本地保留各个参与者之间的时空依赖关系，并为异构数据生成有效的表示。然后，我们提出了一种跨客户端虚拟节点对齐模块，通过多级知识融合方案来纳入跨客户端的时空依赖关系。广泛的隐私分析和实验评估表明，HSTFL不仅能有效抵御推理攻击，而且在面对各种基线方法时还提供了显著的性能提升。
英文摘要：
Spatiotemporal forecasting has emerged as an indispensable building block of diverse smart city applications, such as intelligent transportation and smart energy management. Recent advancements have uncovered that the performance of spatiotemporal forecasting can be significantly improved by integrating knowledge in geo-distributed time series data from different domains, \eg enhancing real-estate appraisal with human mobility data; joint taxi and bike demand predictions. While effective, existing approaches assume a centralized data collection and exploitation environment, overlooking the privacy and commercial interest concerns associated with data owned by different parties. In this paper, we investigate multi-party collaborative spatiotemporal forecasting without direct access to multi-source private data. However, this task is challenging due to 1) cross-domain feature heterogeneity and 2) cross-client geographical heterogeneity, where standard horizontal or vertical federated learning is inapplicable. To this end, we propose a Heterogeneous SpatioTemporal Federated Learning (HSTFL) framework to enable multiple clients to collaboratively harness geo-distributed time series data from different domains while preserving privacy. Specifically, we first devise vertical federated spatiotemporal representation learning to locally preserve spatiotemporal dependencies among individual participants and generate effective representations for heterogeneous data. Then we propose a cross-client virtual node alignment block to incorporate cross-client spatiotemporal dependencies via a multi-level knowledge fusion scheme. Extensive privacy analysis and experimental evaluations demonstrate that HSTFL not only effectively resists inference attacks but also provides a significant improvement against various baselines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
