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<a href="/adlp" class="usa-nav__link">Assoc Director of Laboratory Programs</a>
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<a href="/ctl" class="usa-nav__link">Communications Technology Laboratory</a>
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<a href="/el" class="usa-nav__link">Engineering Laboratory</a>
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<a href="/itl" class="usa-nav__link">Information Technology Laboratory</a>
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<a href="/mml" class="usa-nav__link">Material Measurement Laboratory</a>
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<a href="/pml" class="usa-nav__link">Physical Measurement Laboratory</a>
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<a href="/cnst" class="usa-nav__link">CNST NanoFab</a>
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<a href="/oam" class="usa-nav__link">Office of Advanced Manufacturing</a>
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<a href="/spo" class="usa-nav__link">Special Programs Office</a>
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<a href="/tpo" class="usa-nav__link">Technology Partnerships Office</a>
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<a href="/standardsgov" class="usa-nav__link">Standards.gov</a>
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<h1 class="nist-page__title">Scalability Challenges in Privacy-Preserving Federated Learning</h1>
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<div class="daterange"><time datetime="2024-10-08T12:00:00Z">October 8, 2024</time>
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<span>By: </span>
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<a href="/blogs/cybersecurity-insights/authors/joseph-near" hreflang="en">Joseph Near</a>, <a href="/blogs/cybersecurity-insights/authors/david-darais" hreflang="en">David Darais</a> and <a href="/blogs/cybersecurity-insights/authors/mark-durkee" hreflang="en">Mark Durkee</a>
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<div class="nist-callout"><p><span lang="EN">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 </span><a href="https://www.nist.gov/itl/applied-cybersecurity/privacy-engineering/collaboration-space/blog-series/privacy-preserving"><span lang="EN">NIST’s Privacy Engineering Collaboration Space</span></a><span lang="EN"> or </span><a href="https://rtau.blog.gov.uk/tag/petsblogseries/"><span lang="EN">RTA’s blog</span></a><span lang="EN">. </span></p></div><p><strong>Introduction</strong></p><p>In this post, we talk with <a href="https://cgi.csc.liv.ac.uk/~xiaowei/"><span>Dr. Xiaowei Huang</span></a><span> and </span><a href="https://cgi.csc.liv.ac.uk/~yidong/"><span>Dr. Yi Dong</span></a><span> (University of Liverpool), </span><a href="https://datasciencecampus.ons.gov.uk/author/mat-weldon/"><span>Dr. Mat Weldon</span></a><span> (UK Office of National Statistics (ONS)),</span> and <a href="https://sikhapentyala.github.io/">Sikha Pentyala</a> (University of Washington Tacoma), who were winners in the <a href="https://www.nist.gov/itl/applied-cybersecurity/privacy-engineering/collaboration-space/challenges/us-uk-pets-prize"><span>UK-US PETs Prize Challenges</span></a><span>. We discuss</span> scalability challenges and solutions in privacy-preserving federated learning (PPFL). Traditional federated learning enables scaling to extremely large training datasets by leveraging distributed computing power; adding privacy-preserving techniques can introduce new scalability challenges specific to PPFL.</p><p><strong>Challenges of Scaling Up</strong></p><p>A major challenge of scaling PPFL systems to large datasets and many clients comes from the computational challenges of the cryptography used to implement PPFL defenses. Techniques like fully-homomorphic encryption (FHE) and multiparty computation (MPC) can protect sensitive computations, but often introduce significant performance overhead. In addition, practical PPFL systems often need to work with heterogeneous clients that have different data and computational abilities. The UK-US Prize Challenges were designed to not only test whether solutions offered a strong level of privacy, but also to establish whether they could achieve performance that was practical in real world applications at scale. We asked some of the winners about the scalability challenges that they had to address, and about how the way data is distributed impacts the solutions.</p><p><strong>Authors: How does the distribution of data affect the ability of PPFL systems to scale up?</strong></p><p><strong>Sikha Pentyala, team PPMLHuskies:</strong> One of the biggest gaps is developing general defense techniques for FL with arbitrary data distribution scenarios. This includes data that is horizontally distributed, such as data across different hospitals who each have their own patients, as well as data that is vertically distributed such as in scenarios where a patient's data is distributed across multiple entities (hospitals, companies, labs, etc.). While current literature primarily focuses on horizontal distribution scenarios, less focus has been on vertical or arbitrary distributions. Solutions based on cryptography, such as Secure Multi-Party Computation and Homomorphic Encryption, do work for arbitrary partitions but incur significant computational costs. Developing more efficient algorithms, protocols and tools capable of handling large-scale datasets and complex models is crucial for practical applications with arbitrary partitions.</p><p><strong>Authors: How do differences between clients impact the performance of PPFL systems?</strong></p><p><strong>Sikha Pentyala, team PPMLHuskies: </strong>FL in a heterogeneous cross-device setting can bring additional engineering challenges, in terms of model versioning and synchroni
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>About the author</h2>
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<div class="entity-reference"> <a href="/blogs/cybersecurity-insights/authors/joseph-near" hreflang="en"><img loading="lazy" src="/sites/default/files/styles/thumbnail/public/images/2023/12/06/Jo%20Near.jpg?itok=r50VF0v7" width="86" height="100" alt="Joseph Near" typeof="foaf:Image" />
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<h3 class="nist-teaser__title"><a href="/blogs/cybersecurity-insights/authors/joseph-near"><span>Joseph Near</span>
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<div class="text-with-summary"><p class="MsoBodyText"><span><span><strong>Joseph Near</strong> is an assistant professor of computer science at the University of Vermont who supports NIST as a moderator for the Privacy Engineering Collaboration Space. His research interests include data privacy, computer security, and programming languages. Joseph received his B.S. in computer science from Indiana University, and his M.S. and Ph.D. in computer science from MIT.</span></span></p></div>
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<div class="entity-reference"> <a href="/blogs/cybersecurity-insights/authors/david-darais" hreflang="en"><img loading="lazy" src="/sites/default/files/styles/thumbnail/public/images/2023/12/06/darais-new-640%2B240.jpg?itok=ogWja4KM" width="100" height="100" alt="David Darais" typeof="foaf:Image" />
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<h3 class="nist-teaser__title"><a href="/blogs/cybersecurity-insights/authors/david-darais"><span>David Darais</span>
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<div class="text-with-summary"><p><span><span><span><span><span><span><span><span><strong>David Darais</strong> is a Principal Scientist at Galois, Inc. and supports NIST as a moderator for the Privacy Engineering Collaboration Space. David's research focuses on tools for achieving reliable software in critical, security-sensitive, and privacy-sensitive systems. David received his B.S. from the University of Utah, M.S. from Harvard University and Ph.D. from the University of Maryland.</span></span></span></span></span></span></span></span></p></div>
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<div class="entity-reference"> <a href="/blogs/cybersecurity-insights/authors/mark-durkee" hreflang="en"><img loading="lazy" src="/sites/default/files/styles/thumbnail/public/images/2024/01/24/Mark%20Durkee%20.png?itok=co4ZojG9" width="75" height="100" alt="Mark Durkee " typeof="foaf:Image" />
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<h3 class="nist-teaser__title"><a href="/blogs/cybersecurity-insights/authors/mark-durkee"><span>Mark Durkee</span>
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|
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<div class="text-with-summary"><p><span><span><em><span><span>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.</span></span></em></span></span></p></div>
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