Skip to main content
SHARE
Publication

A Privacy-Aware Federated Learning Framework for Distributed Energy Resource Analytics in Constrained Environments

by Aditya Sundararajan, Robert A Bridges, Mohammed M Olama, Maximiliano F Ferrari Maglia
Publication Type
Conference Paper
Book Title
2023 IEEE PES Innovative Smart Grid Technologies Latin America (ISGT-LA)
Publication Date
Page Numbers
155 to 159
Publisher Location
New Jersey, United States of America
Conference Name
PES Innovative Smart Grid Technologies Latin America (ISGT-LA)
Conference Location
San Juan, Puerto Rico, United States of America
Conference Sponsor
IEEE
Conference Date
-

To be resilient against extreme weather events, the rural communities in Puerto Rico are leveraging distributed energy resources (DER). However, computing frameworks sup-porting the grid in critical decision-making are still largely centralized. Sensitive consumer data are transmitted over the Internet or cellular networks to a secondary or tertiary node. It guarantees better situational awareness at the cost of a wider attack surface, jeopardizing user privacy, as more DER come online. Cloud, Edge, and Fog computing all require data aggregation at some level. This paper introduces a privacy-aware federated learning framework that leverages the Fog model by pushing analytics all the way to the DER and load assets. These local models train on individual asset data and transmit only learned parameters (such as weights) over secure communications to a global decision-maker. By abstracting personally identifiable consumer data without impacting decision optimality, this framework better aligns with distributed power generation paradigm.