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Smart Grid IoT Framework for Predicting Energy Consumption Using Federated Learning Homomorphic Encryption.

Filip Jerkovic1, Nurul I Sarkar1, Jahan Ali1,2

  • 1Department of Computer and Information Sciences, School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland 1010, New Zealand.

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Summary
This summary is machine-generated.

This study introduces a secure Smart Grid (SG) Internet of Things (IoT) framework using federated learning (FL) and homomorphic encryption (HE) to predict energy consumption while protecting user privacy. The novel approach ensures data security in smart grid systems.

Keywords:
edge computingfederated learning (FL)internet of things (IoT)internet privacy and securitymachine learning (ML)smart grid (SG)

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Cybersecurity

Background:

  • Smart Grid (SG) technologies require enhanced data privacy for federated learning (FL) and Internet of Things (IoT) applications.
  • Existing frameworks struggle to balance energy consumption prediction with robust user privacy preservation.

Purpose of the Study:

  • To propose a novel SG IoT framework integrating FL, edge computing, and homomorphic encryption (HE) for secure energy consumption prediction.
  • To ensure end-to-end machine learning workload security and user privacy within smart grid environments.

Main Methods:

  • Developed a framework leveraging federated learning (FL) and edge computing principles.
  • Implemented Cheon-Kim-Kim-Song (CKKS) fully homomorphic encryption (HE) for peer-to-peer (P2P) data exchange between edge devices.
  • Utilized P2P networking for secure communication among edge devices.

Main Results:

  • The proposed framework successfully predicted energy consumption in SG scenarios.
  • Demonstrated effective preservation of user privacy through the integrated HE and FL approach.
  • Validated the robustness and security of the end-to-end machine learning workload execution.

Conclusions:

  • The novel SG IoT framework offers a viable solution for secure energy consumption prediction.
  • Findings provide valuable insights for researchers and engineers developing next-generation SG IoT systems.
  • The integration of FL, edge computing, and HE is crucial for future secure smart grid advancements.