Overview
- Introduces a principled approach to learning from untrusted data sources
- Presents the first systematic overview of robust distributed machine learning
- Equips readers with skills for designing and analyzing robust machine learning algorithms
Part of the book series: Machine Learning: Foundations, Methodologies, and Applications (MLFMA)
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Table of contents (6 chapters)
Keywords
About this book
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.
Authors and Affiliations
About the authors
Rachid Guerraoui is a professor of computer science at EPFL, where he leads the Distributed Computing Laboratory. He has previously worked at the Ecole des Mines de Paris, CEA Saclay, HP Labs in Palo Alto, and MIT. ACM fellow and professor of the College de France, he was awarded a Senior ERC Grant and a Google Focused Award. He has co-authored several popular books on distributed computing, including Reliable and Secure Distributed Programming, and Algorithms for Concurrent Systems.
Nirupam Gupta is a computer science research associate at EPFL. He has previously worked as a postdoc in the department of computer science at Georgetown University. He has served on the program committees of the dependable and secure machine learning workshops at the IEEE DSN conference and the symposium on reliable distributed systems (SRDS), and currently serves as a reviewer for leading control systems and optimization journals, including Elsevier Automatica, IEEE TAC and IEEE CONES. He received his PhD from the University of Maryland College Park, and his bachelor’s degree from the Indian Institute of Technology Delhi.
Rafael Pinot is a junior professor in the department of mathematics at Sorbonne Université, where he holds a chair on the mathematical foundation of computer and data science within the LPSM research unit. He previously worked as a computer science research associate at EPFL and received his PhD from PSL Research University. In 2018, he was awarded a JSPS summer fellowship to join Kyoto University as a visiting researcher. He also received the Dauphine Foundation’s Young Researcher Award (2020) and the Postdoctoral Research Award from EPFL’s Ecocloud Research Center (2021).
Bibliographic Information
Book Title: Robust Machine Learning
Book Subtitle: Distributed Methods for Safe AI
Authors: Rachid Guerraoui, Nirupam Gupta, Rafael Pinot
Series Title: Machine Learning: Foundations, Methodologies, and Applications
DOI: https://doi.org/10.1007/978-981-97-0688-4
Publisher: Springer Singapore
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024
Hardcover ISBN: 978-981-97-0687-7Published: 05 April 2024
Softcover ISBN: 978-981-97-0690-7Due: 22 May 2024
eBook ISBN: 978-981-97-0688-4Published: 04 April 2024
Series ISSN: 2730-9908
Series E-ISSN: 2730-9916
Edition Number: 1
Number of Pages: XVII, 170
Number of Illustrations: 1 b/w illustrations, 11 illustrations in colour
Topics: Machine Learning, Principles and Models of Security, Multiagent Systems, Professional Computing