Robust Machine Learning: Distributed Methods for Safe AI

Guerraoui, Rachid, Gupta, Nirupam, Pinot, Rafael

Robust Machine Learning: Distributed Methods for Safe AI
  • 出版商: Springer
  • 出版日期: 2024-04-05
  • 售價: $6,310
  • 貴賓價: 9.5 折 $5,995
  • 語言: 英文
  • 頁數: 170
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 9819706874
  • ISBN-13: 9789819706877
  • 相關分類: 人工智慧、Machine Learning

    • 海外代購書籍(需單獨結帳)

商品描述

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.


商品描述(中文翻譯)

如今，機器學習算法通常分佈在多台機器上，以利用更多的計算能力和更多的數據。然而，使用分佈式框架會帶來各種安全威脅。特別是，其中一些機器可能會出現異常行為，危及學習過程。這可能是由於硬件和軟件錯誤、數據污染或惡意參與者控制部分機器所導致的。本書以簡單的術語解釋了分佈式機器學習方案對這些威脅的韌性意味，以及如何構建可證明韌性的機器學習算法。

研究機器學習算法的韌性是必要的，因為這些算法在私營和公共部門中無處不在。因此，在過去幾年中，我們見證了關於分佈式機器學習算法韌性的文章數量的快速增長。我們認為現在是為這個新興且動態的領域提供清晰基礎的時候了。通過匯集現有知識並普及韌性的概念，本書為新一代可靠且安全的機器學習方案奠定了基礎。

除了介紹現代機器學習算法中的韌性問題外，本書還將使讀者具備設計具有增強韌性的分佈式學習算法的基本技能。此外，本書為未來在這一領域的研究提供了基礎。

作者簡介

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).


作者簡介(中文翻譯)

Rachid Guerraoui是EPFL的計算機科學教授，他領導著分散式計算實驗室。他曾在巴黎礦業學院、CEA Saclay、帕羅奧圖的HP實驗室和麻省理工學院工作。作為ACM院士和法國學院教授，他獲得了高級ERC獎和Google Focused獎。他與他人合著了幾本關於分散式計算的熱門書籍，包括《可靠且安全的分散式編程》和《並發系統的算法》。

Nirupam Gupta是EPFL的計算機科學研究助理。他曾在喬治城大學的計算機科學系擔任博士後研究員。他曾擔任IEEE DSN會議上可靠且安全的機器學習研討會和可靠分散系統研討會的程序委員會成員，目前還擔任領先的控制系統和優化期刊（包括Elsevier Automatica、IEEE TAC和IEEE CONES）的審稿人。他在馬里蘭大學學院公園獲得博士學位，並在印度理工學院德里獲得學士學位。

Rafael Pinot是巴黎索邦大學數學系的助理教授，他在LPSM研究單位擔任計算機和數據科學的數學基礎主席。他曾在EPFL擔任計算機科學研究助理，並在PSL Research University獲得博士學位。2018年，他獲得了JSPS夏季研究獎學金，作為訪問研究員加入了京都大學。他還獲得了Dauphine基金會的年輕研究員獎（2020年）和EPFL Ecocloud研究中心的博士後研究獎（2021年）。

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