04051nam a22005895i 4500 978-981-97-0688-4 DE-He213 20251030111805.0 cr nn 008mamaa 240404s2024 si | s |||| 0|eng d 9789819706884 978-981-97-0688-4 10.1007/978-981-97-0688-4 doi Q325.5-.7 UYQM bicssc MAT029000 bisacsh UYQM thema Guerraoui, Rachid. author. aut http://id.loc.gov/vocabulary/relators/aut 74748 Robust Machine Learning [electronic resource] : Distributed Methods for Safe AI / by Rachid Guerraoui, Nirupam Gupta, Rafael Pinot. 1st ed. 2024. Singapore : Springer Nature Singapore : Imprint: Springer, 2024. XVII, 170 p. 12 illus., 11 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Machine Learning: Foundations, Methodologies, and Applications, 2730-9916 Chapter 1. Context & Motivation -- Chapter 2. Basics of Machine Learning -- Chapter 3. Federated Machine Learning -- Chapter 4. Fundamentals of Robust Machine Learning -- Chapter 5. Optimal Robustness -- Chapter 6. Practical Robustness. . 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. Studying the robustness of machine learning algorithms is a necessity given the ubiquity of these algorithms in both the private and public sectors. Accordingly, over the past few years, we have witnessed a rapid growth in the number of articles published on the robustness of distributed machine learning algorithms. We believe it is time to provide a clear foundation to this emerging and dynamic field. By gathering the existing knowledge and democratizing the concept of robustness, the book provides the basis for a new generation of reliable and safe machine learning schemes. 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. . Machine learning. 55059 Computer security. 58241 Multiagent systems. Cloud Computing. 58386 Machine Learning. 55059 Principles and Models of Security. 65746 Multiagent Systems. Cloud Computing. 58386 Gupta, Nirupam. author. aut http://id.loc.gov/vocabulary/relators/aut 74749 Pinot, Rafael. author. aut http://id.loc.gov/vocabulary/relators/aut 74750 SpringerLink (Online service) Springer Nature eBook Printed edition: 9789819706877 Printed edition: 9789819706891 Printed edition: 9789819706907 Machine Learning: Foundations, Methodologies, and Applications, 2730-9916 70646 https://doi.org/10.1007/978-981-97-0688-4 ZDB-2-SCS ZDB-2-SXCS 58955 58955