Robust Machine Learning Distributed Methods for Safe AI /

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...

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Opis bibliograficzny
Główni autorzy:	Guerraoui, Rachid (Autor), Gupta, Nirupam (Autor), Pinot, Rafael (Autor)
Korporacja:	SpringerLink (Online service)
Format:	Elektroniczne E-book
Język:	angielski
Wydane:	Singapore : Springer Nature Singapore : Imprint: Springer, 2024.
Wydanie:	1st ed. 2024.
Seria:	Machine Learning: Foundations, Methodologies, and Applications,
Hasła przedmiotowe:	Machine learning. Computer security. Multiagent systems. Cloud Computing. Machine Learning. Principles and Models of Security. Multiagent Systems.
Dostęp online:	https://doi.org/10.1007/978-981-97-0688-4
Etykiety:	Dodaj etykietę Nie ma etykietki, Dołącz pierwszą etykiete!

Spis treści:

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. .

Robust Machine Learning Distributed Methods for Safe AI /

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