FedMTPP: Federated Multivariate Temporal Point Processes for Distributed Event Sequence Forecasting

IF 7.7 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Houxin Gong;Haishuai Wang;Peng Zhang;Sheng Zhou;Hongyang Chen;Jiajun Bu
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引用次数: 0

Abstract

With the rapid development of mobile network technology and wearable mobile devices, user-scenario interactions generate a large amount of user behavioral data in the form of multivariate event sequences. Due to data isolation, these multi-scenario events need to be jointly trained to achieve better prediction results. However, traditional federated learning methods face significant challenges when handling distributed event sequences. And the effectiveness of existing modeling approaches for event sequences in federated contexts has not been thoroughly explored. To this end, we propose Federated Multivariate Temporal Point Processes (FedMTPP), which enables learning from distributed event sequences within a novel federated learning framework and leverages efficient event modeling technology, MTPP, to forecast future events. Specifically, FedMTPP restores the temporal structure of the original event sequence by rearranging event embeddings and redesigns the autoregressive-based hidden representation computation in traditional MTPP, making it more suitable for federated prediction tasks. Additionally, FedMTPP incorporates advanced encryption techniques to effectively safeguard user privacy and security. Experimental results on both synthetic and real datasets demonstrate that FedMTPP substantially improves the performance of local models and achieves results comparable to state-of-the-art centralized MTPP methods.
分布式事件序列预测的联邦多元时间点过程
随着移动网络技术和可穿戴移动设备的快速发展,用户场景交互以多元事件序列的形式产生大量的用户行为数据。由于数据的隔离性,这些多场景事件需要联合训练才能获得更好的预测结果。然而,传统的联邦学习方法在处理分布式事件序列时面临着巨大的挑战。现有的事件序列建模方法的有效性还没有得到充分的探讨。为此,我们提出了联邦多元时间点过程(FedMTPP),它可以在新的联邦学习框架内从分布式事件序列中学习,并利用有效的事件建模技术MTPP来预测未来事件。具体而言,FedMTPP通过重新排列事件嵌入恢复了原始事件序列的时间结构,并重新设计了传统MTPP中基于自回归的隐藏表示计算,使其更适合联邦预测任务。此外,FedMTPP采用先进的加密技术,有效地保护用户的隐私和安全。在合成数据集和真实数据集上的实验结果表明,FedMTPP大大提高了局部模型的性能,并取得了与最先进的集中式MTPP方法相当的结果。
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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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