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020 _a9789819706884
_9978-981-97-0688-4
024 7 _a10.1007/978-981-97-0688-4
_2doi
050 4 _aQ325.5-.7
072 7 _aUYQM
_2bicssc
072 7 _aMAT029000
_2bisacsh
072 7 _aUYQM
_2thema
100 1 _aGuerraoui, Rachid.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_974748
245 1 0 _aRobust Machine Learning
_h[electronic resource] :
_bDistributed Methods for Safe AI /
_cby Rachid Guerraoui, Nirupam Gupta, Rafael Pinot.
250 _a1st ed. 2024.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2024.
300 _aXVII, 170 p. 12 illus., 11 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aMachine Learning: Foundations, Methodologies, and Applications,
_x2730-9916
505 0 _aChapter 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. .
520 _aToday, 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. .
650 0 _aMachine learning.
_955059
650 0 _aComputer security.
_958241
650 0 _aMultiagent systems.
650 0 _aCloud Computing.
_958386
650 1 4 _aMachine Learning.
_955059
650 2 4 _aPrinciples and Models of Security.
_965746
650 2 4 _aMultiagent Systems.
650 2 4 _aCloud Computing.
_958386
700 1 _aGupta, Nirupam.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_974749
700 1 _aPinot, Rafael.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_974750
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789819706877
776 0 8 _iPrinted edition:
_z9789819706891
776 0 8 _iPrinted edition:
_z9789819706907
830 0 _aMachine Learning: Foundations, Methodologies, and Applications,
_x2730-9916
_970646
856 4 0 _uhttps://doi.org/10.1007/978-981-97-0688-4
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
999 _c58955
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