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Privacy-Preserving Federated Learning Blog Series

PPFL Blog Series Picture_Cropped
Credit: NicoElNino/shutterstock.com

As a follow on to the past UK-US PETs Prize Challenges collaboration, we’re excited to share this joint blog series with our UK colleagues focused on federated learning, an approach that addresses the fundamental privacy challenge of traditional machine learning by avoiding the centralized collection of training data. We encourage readers to ask questions and contribute to our work. Please email us at PrivacyEng [at] nist.gov (PrivacyEng[at]nist[dot]gov) or pets [at] dsit.gov.uk (pets[at]dsit[dot]gov[dot]uk)

BLOG POSTS

The UK-US Blog Series on Privacy-Preserving Federated Learning: Introduction | December 7, 2023 | by Joseph Near, David Darais, Naomi Lefkovitz, and Dave Buckley

See Post #1

Privacy Attacks in Federated Learning | January 24, 2024 | by Joseph Near, David Darais, Dave Buckley, and Mark Durkee

See Post #2

Data Distribution in Privacy-Preserving Federated Learning | February 27, 2024 | by David Darais, Joseph Near, Dave Buckley, and Mark Durkee

See Post #3

Next blog post coming soon!

Created November 15, 2023, Updated February 27, 2024