Federated Low-Rank Koopman Learning for Multivariate Time-Series Anomaly Detection in IoT Systems

• URL: http://arxiv.org/abs/2607.08978v1
• Date: Thu, 09 Jul 2026 22:48:24 GMT
• Title: Federated Low-Rank Koopman Learning for Multivariate Time-Series Anomaly Detection in IoT Systems
• Abstract summary: FedKAD is a resource-efficient federated Koopman anomaly detection framework for distributed IoT multivariate time series.<n>It learns normal temporal dynamics through lightweight sliding-window Koopman representations.<n>It provides up to $2.1times103$ faster training, $80times$ lower communication, and $79times$ lower inference latency than neural baselines.
• Score: 21.604249370353585
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Diffuse to Detect: A Generalizable Framework for Anomaly Detection with Diffusion Models Applications to UAVs and Beyond [2.4449457537548036]
  Anomaly detection in complex, high-dimensional data, such as UAV sensor readings, is essential for operational safety.<n>We propose the Diffuse to Detect (DTD) framework, a novel approach that adapts diffusion models for anomaly detection.<n>DTD employs a single-step diffusion process to predict noise patterns, enabling rapid and precise identification of anomalies without reconstruction errors.
  arXiv  Detail & Related papers  (2025-10-27T02:08:08Z)
• Multiresolution Analysis and Statistical Thresholding on Dynamic Networks [49.09073800467438]
  ANIE (Adaptive Network Intensity Estimation) is a multi-resolution framework designed to automatically identify the time scales at which network structure evolves.<n>We show that ANIE adapts to the appropriate time resolution and is able to capture sharp structural changes while remaining robust to noise.
  arXiv  Detail & Related papers  (2025-06-01T22:55:55Z)
• Federated Koopman-Reservoir Learning for Large-Scale Multivariate Time-Series Anomaly Detection [12.44225906937484]
  FedKO is a novel unsupervised Federated Learning framework.<n>It is deployed on edge devices for efficient detection of anomalies in local MVTS streams.<n>It reduces up to 8x communication size and 2x memory usage, making it highly suitable for large-scale systems.
  arXiv  Detail & Related papers  (2025-03-14T10:06:52Z)
• A Novel Spatiotemporal Correlation Anomaly Detection Method Based on Time-Frequency-Domain Feature Fusion and a Dynamic Graph Neural Network in Wireless Sensor Network [9.031267813814118]
  Attention-based transformers have played an important role in wireless sensor network (WSN) timing anomaly detection due to their ability to capture long-term dependencies.<n>This paper proposes a WSN anomaly detection method that integrates frequency-domain features with dynamic graph neural networks (GNN) under a designed self-encoder reconstruction framework.
  arXiv  Detail & Related papers  (2025-02-25T04:34:18Z)
• Towards Resource-Efficient Federated Learning in Industrial IoT for Multivariate Time Series Analysis [50.18156030818883]
  Anomaly and missing data constitute a thorny problem in industrial applications. Deep learning enabled anomaly detection has emerged as a critical direction. The data collected in edge devices contain user privacy.
  arXiv  Detail & Related papers  (2024-11-06T15:38:31Z)
• PeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly Detection [51.20479454379662]
  We propose a. Federated Anomaly Detection framework named PeFAD with the increasing privacy concerns. We conduct extensive evaluations on four real datasets, where PeFAD outperforms existing state-of-the-art baselines by up to 28.74%.
  arXiv  Detail & Related papers  (2024-06-04T13:51:08Z)
• Effective Intrusion Detection in Heterogeneous Internet-of-Things Networks via Ensemble Knowledge Distillation-based Federated Learning [52.6706505729803]
  We introduce Federated Learning (FL) to collaboratively train a decentralized shared model of Intrusion Detection Systems (IDS) FLEKD enables a more flexible aggregation method than conventional model fusion techniques. Experiment results show that the proposed approach outperforms local training and traditional FL in terms of both speed and performance.
  arXiv  Detail & Related papers  (2024-01-22T14:16:37Z)
• Uncertainty-Aware Deep Attention Recurrent Neural Network for Heterogeneous Time Series Imputation [0.25112747242081457]
  Missingness is ubiquitous in multivariate time series and poses an obstacle to reliable downstream analysis. We propose DEep Attention Recurrent Imputation (Imputation), which jointly estimates missing values and their associated uncertainty. Experiments show that I surpasses the SOTA in diverse imputation tasks using real-world datasets.
  arXiv  Detail & Related papers  (2024-01-04T13:21:11Z)
• Correlation-aware Spatial-Temporal Graph Learning for Multivariate Time-series Anomaly Detection [67.60791405198063]
  We propose a correlation-aware spatial-temporal graph learning (termed CST-GL) for time series anomaly detection. CST-GL explicitly captures the pairwise correlations via a multivariate time series correlation learning module. A novel anomaly scoring component is further integrated into CST-GL to estimate the degree of an anomaly in a purely unsupervised manner.
  arXiv  Detail & Related papers  (2023-07-17T11:04:27Z)
• Deep Federated Anomaly Detection for Multivariate Time Series Data [93.08977495974978]
  We present a Federated Exemplar-based Deep Neural Network (Fed-ExDNN) to conduct anomaly detection for multivariate time series data on different edge devices. We show that ExDNN and Fed-ExDNN can outperform state-of-the-art anomaly detection algorithms and federated learning techniques.
  arXiv  Detail & Related papers  (2022-05-09T05:06:58Z)
• Federated Variational Learning for Anomaly Detection in Multivariate Time Series [13.328883578980237]
  We propose an unsupervised time series anomaly detection framework in a federated fashion. We leave the training data distributed at the edge to learn a shared Variational Autoencoder (VAE) based on Convolutional Gated Recurrent Unit (ConvGRU) model. Experiments on three real-world networked sensor datasets illustrate the advantage of our approach over other state-of-the-art models.
  arXiv  Detail & Related papers  (2021-08-18T22:23:15Z)
• Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approach [40.992167455141946]
  This paper proposes a new communication-efficient on-device federated learning (FL)-based deep anomaly detection framework for sensing time-series data in IIoT. We first introduce a FL framework to enable decentralized edge devices to collaboratively train an anomaly detection model, which can improve its generalization ability. Second, we propose an Attention Mechanism-based Convolutional Neural Network-Long Short Term Memory (AMCNN-LSTM) model to accurately detect anomalies. Third, to adapt the proposed framework to the timeliness of industrial anomaly detection, we propose a gradient compression mechanism based on Top-textitk selection to
  arXiv  Detail & Related papers  (2020-07-19T16:47:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.