mlsanityData to 7 Oct 2026

Learning Unknown Interdependencies for Decentralized Root Cause Analysis in Nonlinear Dynamical Systems

arXiv 2602.21928 ↗First posted 25 Feb 2026cs.LG stat.MLcited by 0

Not on the map. Why?

Abstract (arXiv)

Root cause analysis (RCA) in networked industrial systems, such as supply chains and power networks, is notoriously difficult due to unknown and dynamically evolving interdependencies among geographically distributed clients. These clients represent heterogeneous physical processes and industrial assets equipped with sensors that generate large volumes of nonlinear, high-dimensional, and heterogeneous IoT data. Classical RCA methods require partial or full knowledge of the system's dependency graph, which is rarely available in these complex networks. While federated learning (FL) offers a natural framework for decentralized settings, most existing FL methods assume homogeneous feature spaces and retrainable client models. These assumptions are not compatible with our problem setting. Different clients have different data features and often run fixed, proprietary models that cannot be modified. This paper presents a federated cross-client interdependency learning methodology for feature-partitioned, nonlinear time-series data, without requiring access to raw sensor streams or modifying proprietary client models. Each proprietary local client model is augmented with a Machine Learning (ML) model that encodes cross-client interdependencies. These ML models are coordinated via a global server that enforces representation consistency while preserving privacy through calibrated differential privacy noise. RCA is performed using model residuals and anomaly flags. We establish theoretical convergence guarantees and validate our approach on extensive simulations and a real-world industrial cybersecurity dataset.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 27 of its 55 references that are in the map. First posted on arXiv 25 Feb 2026.

Builds on

27 of 55 references.

References · 27

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2017Federated Multi-Task Learningcited by 669
  3. 2018Split learning for health: Distributed deep learning without sharing raw patient datacited by 391
  4. 2021Federated Multi-Task Learning under a Mixture of Distributionscited by 189
  5. 2019SecureBoost: A Lossless Federated Learning Frameworkcited by 174
  6. 2020VAFL: a Method of Vertical Asynchronous Federated Learningcited by 87
  7. 2019Detailed comparison of communication efficiency of split learning and federated learningcited by 76
  8. 2019Split Learning for collaborative deep learning in healthcarecited by 72
  9. 2021Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modelingcited by 69
  10. 2021An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Seriescited by 68
  11. 2019Causal structure based root cause analysis of outlierscited by 65
  12. 2020Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approachcited by 56
15 more
  1. 2022Causal Inference-Based Root Cause Analysis for Online Service Systems with Intervention Recognitioncited by 55
  2. 2021Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updatingcited by 37
  3. 2022Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Datacited by 36
  4. 2021FedGraph: Federated Graph Learning with Intelligent Samplingcited by 34
  5. 2024Root Cause Analysis In Microservice Using Neural Granger Causal Discoverycited by 31
  6. 2023Root Cause Identification for Collective Anomalies in Time Series given an Acyclic Summary Causal Graph with Loopscited by 30
  7. 2023Vertical Federated Graph Neural Network for Recommender Systemcited by 17
  8. 2023FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Modelscited by 17
  9. 2022Coresets for Vertical Federated Learning: Regularized Linear Regression and K-Means Clusteringcited by 14
  10. 2023LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learningcited by 14
  11. 2024Root Cause Analysis for Microservice System based on Causal Inference: How Far Are We?cited by 14
  12. 2014Causal Inference by Identification of Vector Autoregressive Processes with Hidden Componentscited by 11
  13. 2016Estimating Structured Vector Autoregressive Modelcited by 10
  14. 2023Federated Learning of Models Pre-Trained on Different Features with Consensus Graphscited by 8
  15. 2018Root-cause Analysis for Time-series Anomalies via Spatiotemporal Graphical Modeling in Distributed Complex Systemscited by 4

Built on by

Not cited yet.