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Federated Learning and Differential Privacy: What might AI-Enhanced co-design of microelectronics learn?

Eugenio, Evercita C.

Data is a valuable commodity, and it is often dispersed over multiple entities. Sharing data or models created from the data is not simple due to concerns regarding security, privacy, ownership, and model inversion. This limitation in sharing can hinder model training and development. Federated learning can enable data or model sharing across multiple entities that control local data without having to share or exchange the data themselves. Differential privacy is a conceptual framework that brings strong mathematical guarantee for privacy protection and helps provide a quantifiable privacy guarantee to any data or models shared. The concepts of federated learning and differential privacy are introduced along with possible connections. Lastly, some open discussion topics on how federated learning and differential privacy can tied to AI-Enhanced co-design of microelectronics are highlighted.

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TYPE Other Report YEAR 2022
DOIOSTI

Model Validation Database for Fires Involving Fuels at Liquefied Natural Gas Facilities

Luketa, Anay

This document provides a description of the model evaluation protocol (MEP) database for fires involving liquefied natural gas (LNG) and processing fuels at LNG facilities. The purpose of the MEP is to provide procedures regarding the assessment of a model's suitability to predict thermal exclusion zones resulting from a fire. The database includes measurements from pool fire, jet fire, and fireball experiments which are provided in a spreadsheet. Users are to enter model results into the spreadsheet which automatically generates statistical performance measures and graphical comparisons with the experimental data. The intent of this document is to provide a description of the experiments and of the procedure required to carry out the validation portion of the MEP. In addition, the statistical performance measures, measurements for comparisons, and parameter variation are provided.

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TYPE SAND Report YEAR 2022
DOIOSTI

NNSA Minority Serving Institute Partnership Program (MSIPP)--Indigenous Mutual Partnership to Advanced Cybersecurity Technology (ASPIRE, IMPACT and PAMER); FY22 Q2 Progress Report

Atcitty, Stanley; Moriarty, Dylan M.; Hernandez, Virginia

The following report summarizes the status update during this quarter for the National Nuclear Security Agency (NNSA) initiated Minority Serving Institution Partnership Plan's (MSIPP) projects titled, Indigenous Mutual Partnership to Advanced Cybersecurity Technology (ASPIRE), Indigenous Mutual Partnership to Advanced Cybersecurity Technology (IMPACT) and Partnership for Advanced Manufacturing Education and Research (PAMER).

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TYPE SAND Report YEAR 2022
DOIOSTI
Results 7176–7200 of 99,299
Results 7176–7200 of 99,299