Federated Granger Causality Learning for Interdependent Clients with State Space Representation

• URL: http://arxiv.org/abs/2501.13890v2
• Date: Mon, 27 Jan 2025 16:49:19 GMT
• Title: Federated Granger Causality Learning for Interdependent Clients with State Space Representation
• Authors: Ayush Mohanty, Nazal Mohamed, Paritosh Ramanan, Nagi Gebraeel,
• Abstract summary: We develop a federated approach to learning Granger causality. We propose augmenting the client models with the Granger causality information learned by the server. We also study the convergence of the framework to a centralized oracle model.
• Score: 0.6499759302108926
• License:
• Abstract: Advanced sensors and IoT devices have improved the monitoring and control of complex industrial enterprises. They have also created an interdependent fabric of geographically distributed process operations (clients) across these enterprises. Granger causality is an effective approach to detect and quantify interdependencies by examining how one client's state affects others over time. Understanding these interdependencies captures how localized events, such as faults and disruptions, can propagate throughout the system, possibly causing widespread operational impacts. However, the large volume and complexity of industrial data pose challenges in modeling these interdependencies. This paper develops a federated approach to learning Granger causality. We utilize a linear state space system framework that leverages low-dimensional state estimates to analyze interdependencies. This addresses bandwidth limitations and the computational burden commonly associated with centralized data processing. We propose augmenting the client models with the Granger causality information learned by the server through a Machine Learning (ML) function. We examine the co-dependence between the augmented client and server models and reformulate the framework as a standalone ML algorithm providing conditions for its sublinear and linear convergence rates. We also study the convergence of the framework to a centralized oracle model. Moreover, we include a differential privacy analysis to ensure data security while preserving causal insights. Using synthetic data, we conduct comprehensive experiments to demonstrate the robustness of our approach to perturbations in causality, the scalability to the size of communication, number of clients, and the dimensions of raw data. We also evaluate the performance on two real-world industrial control system datasets by reporting the volume of data saved by decentralization.

Related papers

• Modality Alignment Meets Federated Broadcasting [9.752555511824593]
  Federated learning (FL) has emerged as a powerful approach to safeguard data privacy by training models across distributed edge devices without centralizing local data. This paper introduces a novel FL framework leveraging modality alignment, where a text encoder resides on the server, and image encoders operate on local devices.
  arXiv  Detail & Related papers  (2024-11-24T13:30:03Z)
• Targeted Cause Discovery with Data-Driven Learning [66.86881771339145]
  We propose a novel machine learning approach for inferring causal variables of a target variable from observations. We employ a neural network trained to identify causality through supervised learning on simulated data. Empirical results demonstrate the effectiveness of our method in identifying causal relationships within large-scale gene regulatory networks.
  arXiv  Detail & Related papers  (2024-08-29T02:21:11Z)
• Enhancing Federated Learning with Adaptive Differential Privacy and Priority-Based Aggregation [0.0]
  Federated learning (FL) develops global models without direct access to local datasets. It is possible to access the model updates transferred between clients and servers, potentially revealing sensitive local information to adversaries. Differential privacy (DP) offers a promising approach to addressing this issue by adding noise to the parameters. We propose a personalized DP framework that injects noise based on clients' relative impact factors and aggregates parameters.
  arXiv  Detail & Related papers  (2024-06-26T16:55:07Z)
• 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)
• An Aggregation-Free Federated Learning for Tackling Data Heterogeneity [50.44021981013037]
  Federated Learning (FL) relies on the effectiveness of utilizing knowledge from distributed datasets. Traditional FL methods adopt an aggregate-then-adapt framework, where clients update local models based on a global model aggregated by the server from the previous training round. We introduce FedAF, a novel aggregation-free FL algorithm.
  arXiv  Detail & Related papers  (2024-04-29T05:55:23Z)
• Federated Learning with Uncertainty-Based Client Clustering for Fleet-Wide Fault Diagnosis [5.684489493030555]
  Federated learning has emerged as a promising solution to leverage datasets from multiple operators to train a decentralized asset fault diagnosis model. We propose a novel clustering-based FL algorithm where clients are clustered for federating based on dataset similarity.
  arXiv  Detail & Related papers  (2023-04-26T04:23:59Z)
• Towards Understanding and Mitigating Dimensional Collapse in Heterogeneous Federated Learning [112.69497636932955]
  Federated learning aims to train models across different clients without the sharing of data for privacy considerations. We study how data heterogeneity affects the representations of the globally aggregated models. We propose sc FedDecorr, a novel method that can effectively mitigate dimensional collapse in federated learning.
  arXiv  Detail & Related papers  (2022-10-01T09:04:17Z)
• Federated Stochastic Gradient Descent Begets Self-Induced Momentum [151.4322255230084]
  Federated learning (FL) is an emerging machine learning method that can be applied in mobile edge systems. We show that running to the gradient descent (SGD) in such a setting can be viewed as adding a momentum-like term to the global aggregation process.
  arXiv  Detail & Related papers  (2022-02-17T02:01:37Z)
• Federated Causal Discovery [74.37739054932733]
  This paper develops a gradient-based learning framework named DAG-Shared Federated Causal Discovery (DS-FCD) It can learn the causal graph without directly touching local data and naturally handle the data heterogeneity. Extensive experiments on both synthetic and real-world datasets verify the efficacy of the proposed method.
  arXiv  Detail & Related papers  (2021-12-07T08:04:12Z)
• Graph-based Incident Aggregation for Large-Scale Online Service Systems [33.70557954446136]
  We propose GRLIA, an incident aggregation framework based on graph representation learning over the cascading graph of cloud failures. A representation vector is learned for each unique type of incident in an unsupervised and unified manner, which is able to simultaneously encode the topological and temporal correlations. The proposed framework is evaluated with real-world incident data collected from a large-scale online service system of Huawei Cloud.
  arXiv  Detail & Related papers  (2021-08-27T08:48:55Z)

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.