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Federated Bounded-Memory Anytime-Valid Kernel Independence Testing for Structural Equation Models in Non-Stationary IoT Anomaly Detection
Subject area: Science,Engineering and Technology · Area of research: Internet of Things
Abstract
Federated Internet of Things deployments require anomaly detectors that respect strict memory limits while remaining statistically valid under non-stationary data streams. Conventional kernel independence tests, such as the Hilbert-Schmidt Independence Criterion, demand the retention of full Gram matrices, and their batch formulation cannot support sequential decision making; consequently, they are ill-suited to edge devices whose distributions drift over time. We propose a federated bounded-memory framework for anytime-valid kernel independence testing within structural equation models. Each node first embeds sensor variables and model residuals into a finite-dimensional Euclidean space through random Fourier features, and then maintains a decayed cross-covariance sketch whose memory footprint scales only with the square of the feature dimension. Anytime-valid e-values are constructed from a martingale wealth process driven by betting fractions, so that the false-alarm rate is controlled at a prescribed level irrespective of the stopping time. Furthermore, only quantized adjacency updates, compressed covariance sketches, and sample counts are transmitted to the server, where a weighted consensus update refines the global causal graph and shared structural parameters. The proposed method replaces the batch-based conditional independence tester in the causal inference engine, and its continuous e-value output feeds the anomaly scoring module and the federated graph coordinator, thereby closing the loop between causal discovery and equation fitting. Experiments on non-stationary IoT benchmarks assess detection accuracy, communication cost and memory consumption. The framework attains constant memory usage and linear per-step time complexity, and it therefore offers a practical route to rigorous, privacy-preserving causal anomaly detection at the network edge.
Keywords
federated learning; kernel independence testing; structural equation models; non-stationary IoT anomaly detection; anytime-valid inference
References
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How to cite this paper
@article{1724286,
author = {Dr. Rahul Misra},
title = {Federated Bounded-Memory Anytime-Valid Kernel Independence Testing for Structural Equation Models in Non-Stationary IoT Anomaly Detection},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {4},
pages = {4173-4185},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1724286.pdf},
abstract = {Federated Internet of Things deployments require anomaly detectors that respect strict memory limits while remaining statistically valid under non-stationary data streams. Conventional kernel independence tests, such as the Hilbert-Schmidt Independence Criterion, demand the retention of full Gram matrices, and their batch formulation cannot support sequential decision making; consequently, they are ill-suited to edge devices whose distributions drift over time. We propose a federated bounded-memory framework for anytime-valid kernel independence testing within structural equation models. Each node first embeds sensor variables and model residuals into a finite-dimensional Euclidean space through random Fourier features, and then maintains a decayed cross-covariance sketch whose memory footprint scales only with the square of the feature dimension. Anytime-valid e-values are constructed from a martingale wealth process driven by betting fractions, so that the false-alarm rate is controlled at a prescribed level irrespective of the stopping time. Furthermore, only quantized adjacency updates, compressed covariance sketches, and sample counts are transmitted to the server, where a weighted consensus update refines the global causal graph and shared structural parameters. The proposed method replaces the batch-based conditional independence tester in the causal inference engine, and its continuous e-value output feeds the anomaly scoring module and the federated graph coordinator, thereby closing the loop between causal discovery and equation fitting. Experiments on non-stationary IoT benchmarks assess detection accuracy, communication cost and memory consumption. The framework attains constant memory usage and linear per-step time complexity, and it therefore offers a practical route to rigorous, privacy-preserving causal anomaly detection at the network edge.},
keywords = {federated learning; kernel independence testing; structural equation models; non-stationary IoT anomaly detection; anytime-valid inference},
month = {October},
}