Public Abstract

DE-SC0023392: Building a federated learning framework for trustworthy and resilient energy internet of things (eIOT) infrastructure

Award Status: Active

Institution: The Regents of New Mexico State University (NMSU, Las Cruces), Las Cruces, NM
UEI: J3M5GZAT8N85
DUNS: 173851965

Most Recent Award Date: 07/26/2024
Number of Support Periods: 3
PM: Fitzsimmons, Timothy

Current Budget Period: 09/01/2024 - 08/31/2025
Current Project Period: 09/01/2022 - 08/31/2025
PI: Misra, Satyajayant

Supplement Budget Period: N/A

Public Abstract

The “energy internet of things” (eIoT) is a promising technical aid for the energy-management change drivers, such as rising demand for electricity, the prominence of clean and distributed energy resources (DERs), the emergence of electrified transportation, deregulation of power markets, and innovations in smart grid technology. The convergence of cyber, physical, and economic frameworks in the energy sector depends primarily on the distributed heterogeneous eIoT devices and their collaborative management. These eIoT devices will increasingly get integrated into the power grid providing unparalleled visibility into energy generation, use, and operations. This proliferation will create a deluge of operational and information technology data, from geographically diverse and heterogeneous sources, necessitating use of data-driven machine learning (ML) applications. The ML applications will digest the data volume, preferably close to the devices themselves, and help increase grid scalability and efficiency. Given the data volumes, the need for resilience, and user/data privacy needs, federated machine learning (FML) can be used as a foundation to improve not only visibility and optimization, but also security of the eIoT infrastructure. Methods: In this project, motivated by this we aim to be the first to propose a foundational blueprint for secure, verifiable, and privacy preserving FML frameworks that can be used to build the trustworthy and resilient eIoT infrastructure of tomorrow. We aim basic innovation on five research dimensions. The first is the creation of a distributed public key infrastructure, utilizing the certificate transparency standards, to handle the diversity of the devices (from low-capability legacy devices to capable computing devices), while also enabling seamless onboarding/offboarding of devices and resilient key management. The second dimension builds a verifiable and trusted execution framework for device operations, which enables trustworthy insight exchanges under a zero-trust setting. In the third, we utilize the first two dimensions as foundations to develop a use-case inspired FML framework for eIoT, to be studied in the context of short-term energy forecasting. The framework accommodates parameters exchange within the ambit of communication infrastructure constraints while preserving user/data privacy through differential privacy (DP). The framework will be generalizable to other use cases. We address anomaly detection and coordinated attack mitigation in the fourth dimension by significantly enhancing the generic FML framework built in the third dimension. In the fifth, we develop a co-simulation framework and emulation/testbed functionalities both locally and with help of our collaborators—National Renewable Energy Laboratory (NREL) and Microgrid Systems Laboratory (MSL)—to validate our algorithms, protocols, and the consequent overall eIoT infrastructure. Outcomes and Impacts: The research impact will be on two timescales. In the short term, the project will build the basic foundations for distributed identity and trust management in the eIoT. Using this foundation, we will build trustworthy FML frameworks for increasing eIoT efficiency and resilience. This innovation will help move the needle on broader application of the eIoT concept. The four graduate students will have the opportunity to work with several researchers at NMSU and with collaborators at NREL and MSL via internships, externships, and visits to get a well-rounded understanding of the field to make innovations. In the long term, the ideas disseminated through peer-reviewed publications, workshops, and presentations, will not only encourage basic research in the eIoT area, but also germinate similar basic research in allied areas of cyberphysical systems. The opportunity will cement the inter-institutional collaboration and create a pipeline to recruit students from traditionally underrepresented populations, train them in the area, and prepare them for cybersecurity and smart grid careers. This workforce creation will serve a critical need of the nation and is also a key mission of NMSU, a land grant, Hispanic serving institution.

The “energy internet of things” (eIoT) is a promising technical aid for the energy-management change drivers, such as rising demand for electricity, the prominence of clean and distributed energy resources (DERs), the emergence of electrified transportation, deregulation of power markets, and innovations in smart grid technology. The convergence of cyber, physical, and economic frameworks in the energy sector depends primarily on the distributed heterogeneous eIoT devices and their collaborative management. These eIoT devices will increasingly get integrated into the power grid providing unparalleled visibility into energy generation, use, and operations. This proliferation will create a deluge of operational and information technology data, from geographically diverse and heterogeneous sources, necessitating use of data-driven machine learning (ML) applications. The ML applications will digest the data volume, preferably close to the devices themselves, and help increase grid scalability and efficiency. Given the data volumes, the need for resilience, and user/data privacy needs, federated machine learning (FML) can be used as a foundation to improve not only visibility and optimization, but also security of the eIoT infrastructure.

Methods: In this project, motivated by this we aim to be the first to propose a foundational blueprint for secure, verifiable, and privacy preserving FML frameworks that can be used to build the trustworthy and resilient eIoT infrastructure of tomorrow. We aim basic innovation on five research dimensions. The first is the creation of a distributed public key infrastructure, utilizing the certificate transparency standards, to handle the diversity of the devices (from low-capability legacy devices to capable computing devices), while also enabling seamless onboarding/offboarding of devices and resilient key management. The second dimension builds a verifiable and trusted execution framework for device operations, which enables trustworthy insight exchanges under a zero-trust setting. In the third, we utilize the first two dimensions as foundations to develop a use-case inspired FML framework for eIoT, to be studied in the context of short-term energy forecasting. The framework accommodates parameters exchange within the ambit of communication infrastructure constraints while preserving user/data privacy through differential privacy (DP). The framework will be generalizable to other use cases. We address anomaly detection and coordinated attack mitigation in the fourth dimension by significantly enhancing the generic FML framework built in the third dimension. In the fifth, we develop a co-simulation framework and emulation/testbed functionalities both locally and with help of our collaborators—National Renewable Energy Laboratory (NREL) and Microgrid Systems Laboratory (MSL)—to validate our algorithms, protocols, and the consequent overall eIoT infrastructure.

Outcomes and Impacts: The research impact will be on two timescales. In the short term, the project will build the basic foundations for distributed identity and trust management in the eIoT. Using this foundation, we will build trustworthy FML frameworks for increasing eIoT efficiency and resilience. This innovation will help move the needle on broader application of the eIoT concept. The four graduate students will have the opportunity to work with several researchers at NMSU and with collaborators at NREL and MSL via internships, externships, and visits to get a well-rounded understanding of the field to make innovations. In the long term, the ideas disseminated through peer-reviewed publications, workshops, and presentations, will not only encourage basic research in the eIoT area, but also germinate similar basic research in allied areas of cyberphysical systems. The opportunity will cement the inter-institutional collaboration and create a pipeline to recruit students from traditionally underrepresented populations, train them in the area, and prepare them for cybersecurity and smart grid careers. This workforce creation will serve a critical need of the nation and is also a key mission of NMSU, a land grant, Hispanic serving institution.