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

Technical Report · Sun May 01 04:00:00 EDT 2022

DOI:https://doi.org/10.2172/1868417· OSTI ID:1868417

Eugenio, Evercita C. ^[1]

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1. Sandia National Laboratories (SNL), Albuquerque, NM, and Livermore, CA (United States)

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.

• Research Organization: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA)
• DOE Contract Number: NA0003525
• OSTI ID: 1868417
• Report Number(s): SAND2022-6081R; 706343
• Country of Publication: United States
• Language: English

Related Subjects

29 ENERGY PLANNING, POLICY, AND ECONOMY
97 MATHEMATICS AND COMPUTING