Hao Miao;Yan Zhao;Chenjuan Guo;Bin Yang;Kai Zheng;Christian S. Jensen
{"title":"Spatio-Temporal Prediction on Streaming Data: A Unified Federated Continuous Learning Framework","authors":"Hao Miao;Yan Zhao;Chenjuan Guo;Bin Yang;Kai Zheng;Christian S. Jensen","doi":"10.1109/TKDE.2025.3528876","DOIUrl":null,"url":null,"abstract":"The widespread deployment of wireless and mobile devices results in a proliferation of decentralized spatio-temporal data. Many recent proposals that target deep learning for spatio-temporal prediction assume that all data is available at a central location and suffers from so-called catastrophic forgetting, where previously learned knowledge is entirely forgotten when new data arrives. Such proposals may face data privacy concerns and may experience deteriorating prediction performance when applied in decentralized settings where data streams into the system. To bridge the gap between decentralized training and spatio-temporal prediction on streaming data, we propose a unified federated continuous learning framework, which uses a horizontal federated learning mechanism for protecting data privacy and includes a global replay buffer with synthetic spatio-temporal data generated by the previously learned global model. For each client, we fuse the current training data with synthetic spatio-temporal data using a spatio-temporal mixup mechanism to preserve historical knowledge effectively, thus avoiding catastrophic forgetting. To enable holistic representation preservation, the local models at clients each integrates a general spatio-temporal autoencoder with a spatio-temporal simple siamese network that aims to ensure prediction accuracy and avoid holistic feature loss. Extensive experiments on real data offer insight into the effectiveness of the proposed framework.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 4","pages":"2126-2140"},"PeriodicalIF":8.9000,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Knowledge and Data Engineering","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10840235/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
The widespread deployment of wireless and mobile devices results in a proliferation of decentralized spatio-temporal data. Many recent proposals that target deep learning for spatio-temporal prediction assume that all data is available at a central location and suffers from so-called catastrophic forgetting, where previously learned knowledge is entirely forgotten when new data arrives. Such proposals may face data privacy concerns and may experience deteriorating prediction performance when applied in decentralized settings where data streams into the system. To bridge the gap between decentralized training and spatio-temporal prediction on streaming data, we propose a unified federated continuous learning framework, which uses a horizontal federated learning mechanism for protecting data privacy and includes a global replay buffer with synthetic spatio-temporal data generated by the previously learned global model. For each client, we fuse the current training data with synthetic spatio-temporal data using a spatio-temporal mixup mechanism to preserve historical knowledge effectively, thus avoiding catastrophic forgetting. To enable holistic representation preservation, the local models at clients each integrates a general spatio-temporal autoencoder with a spatio-temporal simple siamese network that aims to ensure prediction accuracy and avoid holistic feature loss. Extensive experiments on real data offer insight into the effectiveness of the proposed framework.
期刊介绍:
The IEEE Transactions on Knowledge and Data Engineering encompasses knowledge and data engineering aspects within computer science, artificial intelligence, electrical engineering, computer engineering, and related fields. It provides an interdisciplinary platform for disseminating new developments in knowledge and data engineering and explores the practicality of these concepts in both hardware and software. Specific areas covered include knowledge-based and expert systems, AI techniques for knowledge and data management, tools, and methodologies, distributed processing, real-time systems, architectures, data management practices, database design, query languages, security, fault tolerance, statistical databases, algorithms, performance evaluation, and applications.