Data Distribution in Privacy-Preserving Federated Learning

This post is part of a series on privacy-preserving federated learning. The series is a collaboration between NIST and the UK government’s Responsible Technology Adoption Unit (RTA), previously known as the Centre for Data Ethics and Innovation. Learn more and read all the posts published to date at NIST’s Privacy Engineering Collaboration Space or RTA’s blog . Our first post in the series introduced the concept of federated learning and described how it’s different from traditional centralized learning - in federated learning, the data is distributed among participating organizations, and

Mark Durkee

*Mark Durkee is Head of Data & Technology at the Centre for Data Ethics and Innovation (CDEI). He leads a portfolio of work including the CDEI's work on the Algorithmic Transparency Recording Standard, privacy enhancing technologies, and a broader programme of work focused on promoting responsible access to data. He previously led CDEI's independent review into bias in algorithmic decision-making. He has spent over a decade working in a variety of technology strategy, architecture and cyber security roles within the UK government, and previously worked as a software engineer and completed a PhD in Theoretical Physics.*

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