A Framework for Edge Intelligent Smart Distribution Grids via Federated Learning

Nathaniel Hudson, Md Jakir Hossain, Minoo Hosseinzadeh, Hana Khamfroush, Mahshid Rahnamay-Naeini, Nasir Ghani

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

Recent advances in distributed data processing and machine learning provide new opportunities to enable critical, time-sensitive functionalities of smart distribution grids in a secure and reliable fashion. Combining the recent advents of edge computing (EC) and edge intelligence (EI) with existing advanced metering infrastructure (AMI) has the potential to reduce overall communication cost, preserve user privacy, and provide improved situational awareness. In this paper, we provide an overview for how EC and EI can supplement applications relevant to AMI systems. Additionally, using such systems in tandem can enable distributed deep learning frameworks (e.g., federated learning) to empower distributed data processing and intelligent decision making for AMI. Finally, to demonstrate the efficacy of this considered architecture, we approach the non-intrusive load monitoring (NILM) problem using federated learning to train a deep recurrent neural network architecture in a 2-tier and 3-tier manner. In this approach, smart homes locally train a neural network using their metering data and only share the learned model parameters with AMI components for aggregation. Our results show this can reduce communication cost associated with distributed learning, as well as provide an immediate layer of privacy, due to no raw data being communicated to AMI components. Further, we show that FL is able to closely match the model loss provided by standard centralized deep learning where raw data is communicated for centralized training.

Original languageEnglish
Title of host publication30th International Conference on Computer Communications and Networks, ICCCN 2021
ISBN (Electronic)9780738113302
DOIs
StatePublished - Jul 2021
Event30th International Conference on Computer Communications and Networks, ICCCN 2021 - Virtual, Athens, Greece
Duration: Jul 19 2021Jul 22 2021

Publication series

NameProceedings - International Conference on Computer Communications and Networks, ICCCN
Volume2021-July
ISSN (Print)1095-2055

Conference

Conference30th International Conference on Computer Communications and Networks, ICCCN 2021
Country/TerritoryGreece
CityVirtual, Athens
Period7/19/217/22/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

Keywords

  • Advanced metering infrastructure
  • Cyber-physical systems
  • Distribution network
  • Edge Computing
  • Federated learning
  • Non-intrusive Load Monitoring (NILM)
  • Smart grid

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Software

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