物联网系统中用于多变量时间序列异常检测的联邦低秩库普曼学习

中文摘要 AI 辅助

分布式物联网系统生成多变量时间序列流以监测物理资产、服务器和嵌入式传感平台。检测异常时间行为对故障诊断、预测性维护和安全至关重要。但实际物联网异常检测受分散和非IID数据、有限带宽以及边缘设备计算和内存受限的阻碍。本文提出FedKAD，一种用于分布式物联网多变量时间序列的资源高效联邦库普曼异常检测框架。与基于深度学习的异常检测器不同，FedKAD通过轻量级滑动窗口库普曼表示学习正常时间动态。联邦训练被公式化为低秩共识问题，原始传感器流和局部简化动态保留在设备上，仅紧凑子空间变量与服务器交换。为在正交性约束下优化共享表示，开发了联邦Stiefel - ADMM算法并提供部分客户端参与下的收敛和平稳性分析。推理时，每个客户端通过测量观察到的未来轨迹与学习到的库普曼动态之间的预测残差在本地检测异常。在四个广泛使用的多变量时间序列异常检测基准上的实验表明，与联邦深度学习基线相比，FedKAD保持或提高了检测性能。更重要的是，对于物联网部署，FedKAD比神经基线提供快达$2.1\times10^3$倍的训练速度、低80倍的通信量和低79倍的推理延迟，证实其适用于资源受限的边缘设备。

英文摘要

Distributed IoT systems generate multivariate time-series streams for monitoring physical assets, servers, and embedded sensing platforms. Detecting abnormal temporal behavior is critical for fault diagnosis, predictive maintenance, and security. However, practical IoT anomaly detection is hindered by decentralized and non-IID data, limited bandwidth, and the constrained computation and memory of edge devices. This paper proposes FedKAD, a resource-efficient federated Koopman anomaly detection framework for distributed IoT multivariate time series. Unlike deep-learning-based anomaly detectors that require training and communicating large neural models, FedKAD learns normal temporal dynamics through lightweight sliding-window Koopman representations. Federated training is formulated as a low-rank consensus problem, where raw sensor streams and local reduced dynamics remain on device while only compact subspace variables are exchanged with the server. To optimize the shared representation under orthonormality constraints, we develop a federated Stiefel-ADMM algorithm and provide convergence and stationarity analysis under partial client participation. During inference, each client detects anomalies locally by measuring the prediction residual between observed future trajectories and the learned Koopman dynamics. Experiments on four widely used multivariate time-series anomaly detection benchmarks show that FedKAD maintains or improves detection performance compared with federated deep-learning baselines. More importantly for IoT deployment, FedKAD provides up to $2.1\times10^3$ faster training, $80\times$ lower communication, and $79\times$ lower inference latency than neural baselines, confirming its suitability for resource-constrained edge devices.

发表机构

• University of Technology Sydney（悉尼科技大学）
• FPT University（越南邮电大学）
• Hanoi University of Science and Technology（河内科技大学）

机构由 AI 辅助整理，请以论文原文为准。