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Efficient Decentralized Federated Singular Vector Decomposition

Offered By: USENIX via YouTube

Tags

Singular Value Decomposition Courses Data Analysis Courses Machine Learning Courses Linear Algebra Courses Federated Learning Courses Matrix Factorization Courses Distributed Computing Courses Decentralized Systems Courses Homomorphic Encryption Courses

Course Description

Overview

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Explore a 19-minute conference talk from USENIX ATC '24 on Efficient Decentralized Federated Singular Vector Decomposition. Delve into the innovative Excalibur system, which addresses the challenges of federated singular value decomposition (SVD) in distributed applications. Learn how researchers from Hong Kong University of Science and Technology and Peking University developed a lightweight matrix protection method and a communication-efficient decentralized SVD workflow to overcome privacy and efficiency issues. Discover how Excalibur eliminates the need for external servers while achieving 3.1× to 6.0× faster performance than state-of-the-art server-aided methods on billion-scale data. Gain insights into the system's impressive throughput, which is over 23000× larger than current homomorphic encryption-based systems.

Syllabus

USENIX ATC '24 - Efficient Decentralized Federated Singular Vector Decomposition


Taught by

USENIX

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