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August 17, 2025International Academic Journal of Innovative Research

VoltSecure: A Secure Federated Learning Model for Decentralized Energy Management Systems

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Authors

QHQ. Hugh

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Overview

Observational analysis shows VoltSecure enhances data privacy and security in decentralized energy management systems, suggesting improved scalability and efficiency.

Key Points

  • VoltSecure improves energy management by ensuring data privacy and reducing communication burden, enabling real-time responsiveness.
  • The framework demonstrates high prediction accuracy and robust defense against cyber threats, utilizing local model updates.
  • Implementation of federated learning allows smart grid nodes to collaborate securely without sharing sensitive data across networks.
  • Efficiency in communication costs is confirmed, making VoltSecure suitable for environments with limited bandwidth.

Cite This Study

Q. Hugh (2025) studied this question.

synapsesocial.com/papers/68a36a480a429f797332ec9ehttps://doi.org/10.71086/iajir/v12i3/iajir1223
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Federated Learning for Enhancing Cybersecurity Resilience in Distributed Energy Systems2025
  2. 2Federated Learning Architectures for Privacy-Preserving Smart Grid Data Processing2025
  3. 3Towards a cybersecure and privacy enhanced smart grid: A blockchain enabled federated learning framework2026 · 1 citations
  4. 4Federated Learning for Smart Grids: Mathematical Formulation and Algorithms2025
  5. 5Federated Learning for Secure Industrial Automation and Grid Optimization2026