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Robust Machine Learning: Distributed Methods for Safe AI
Hardcover
Series: Machine Learning: Foundations, Methodologies, and Applications
General ComputersProbability & StatisticsComputer Security
ISBN13: 9789819706877
Publisher: Springer
Published: Apr 5 2024
Pages: 170
Weight: 0.98
Height: 0.50 Width: 6.14 Depth: 9.21
Language: English
Today, machine learning algorithms are often distributed across multiple machines to leverage more computing power and more data. However, the use of a distributed framework entails a variety of security threats. In particular, some of the machines may misbehave and jeopardize the learning procedure. This could, for example, result from hardware and software bugs, data poisoning or a malicious player controlling a subset of the machines. This book explains in simple terms what it means for a distributed machine learning scheme to be robust to these threats, and how to build provably robust machine learning algorithms.
In addition to introducing the problem of robustness in modern machine learning algorithms, the book will equip readers with essential skills for designing distributed learning algorithms with enhanced robustness. Moreover, the book provides a foundation for future research in this area.
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