Federated Learning: A Comprehensive Overview of Methods and Applications
暫譯: 聯邦學習：方法與應用的全面概述
Ludwig, Heiko, Baracaldo, Nathalie
- 出版商: Springer
- 出版日期: 2023-07-09
- 售價: $6,780
- 貴賓價: 9.5 折 $6,441
- 語言: 英文
- 頁數: 534
- 裝訂: Quality Paper - also called trade paper
- ISBN: 3030968987
- ISBN-13: 9783030968984
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相關翻譯:
聯邦學習 (簡中版)
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其他版本:
Federated Learning: A Comprehensive Overview of Methods and Applications
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商品描述
Federated Learning: A Comprehensive Overview of Methods and Applications presents an in-depth discussion of the most important issues and approaches to federated learning for researchers and practitioners.
Federated Learning (FL) is an approach to machine learning in which the training data are not managed centrally. Data are retained by data parties that participate in the FL process and are not shared with any other entity. This makes FL an increasingly popular solution for machine learning tasks for which bringing data together in a centralized repository is problematic, either for privacy, regulatory or practical reasons.
This book explains recent progress in research and the state-of-the-art development of Federated Learning (FL), from the initial conception of the field to first applications and commercial use. To obtain this broad and deep overview, leading researchers address the different perspectives of federated learning: the core machine learning perspective, privacy and security, distributed systems, and specific application domains. Readers learn about the challenges faced in each of these areas, how they are interconnected, and how they are solved by state-of-the-art methods.
Following an overview on federated learning basics in the introduction, over the following 24 chapters, the reader will dive deeply into various topics. A first part addresses algorithmic questions of solving different machine learning tasks in a federated way, how to train efficiently, at scale, and fairly. Another part focuses on providing clarity on how to select privacy and security solutions in a way that can be tailored to specific use cases, while yet another considers the pragmatics of the systems where the federated learning process will run. The book also covers other important use cases for federated learning such as split learning and vertical federated learning. Finally, the book includes some chapters focusing on applying FL in real-world enterprise settings.
Federated Learning (FL) is an approach to machine learning in which the training data are not managed centrally. Data are retained by data parties that participate in the FL process and are not shared with any other entity. This makes FL an increasingly popular solution for machine learning tasks for which bringing data together in a centralized repository is problematic, either for privacy, regulatory or practical reasons.
This book explains recent progress in research and the state-of-the-art development of Federated Learning (FL), from the initial conception of the field to first applications and commercial use. To obtain this broad and deep overview, leading researchers address the different perspectives of federated learning: the core machine learning perspective, privacy and security, distributed systems, and specific application domains. Readers learn about the challenges faced in each of these areas, how they are interconnected, and how they are solved by state-of-the-art methods.
Following an overview on federated learning basics in the introduction, over the following 24 chapters, the reader will dive deeply into various topics. A first part addresses algorithmic questions of solving different machine learning tasks in a federated way, how to train efficiently, at scale, and fairly. Another part focuses on providing clarity on how to select privacy and security solutions in a way that can be tailored to specific use cases, while yet another considers the pragmatics of the systems where the federated learning process will run. The book also covers other important use cases for federated learning such as split learning and vertical federated learning. Finally, the book includes some chapters focusing on applying FL in real-world enterprise settings.
商品描述(中文翻譯)
《聯邦學習：方法與應用的綜合概述》深入探討了聯邦學習中最重要的議題和方法，適合研究人員和實務工作者參考。聯邦學習（Federated Learning, FL）是一種機器學習方法，其中訓練數據不由中央管理。數據由參與FL過程的數據方保留，並不與其他實體共享。這使得FL成為一種越來越受歡迎的解決方案，特別是對於那些因隱私、法規或實際原因而無法將數據集中到一個中央庫中的機器學習任務。
本書解釋了聯邦學習（FL）研究的最新進展及其最先進的發展，從該領域的初步概念到首次應用和商業使用。為了獲得這一廣泛而深入的概述，領先的研究人員從不同的角度探討聯邦學習：核心的機器學習視角、隱私與安全、分散式系統以及特定的應用領域。讀者將了解在這些領域中面臨的挑戰、它們之間的相互聯繫，以及如何通過最先進的方法來解決這些挑戰。
在介紹中對聯邦學習基礎進行概述後，接下來的24章將深入探討各種主題。第一部分針對以聯邦方式解決不同機器學習任務的算法問題，如何高效、可擴展且公平地進行訓練。另一部分則專注於如何選擇隱私和安全解決方案，以便根據特定用例進行調整，而另一部分則考慮聯邦學習過程運行的系統的實用性。本書還涵蓋了聯邦學習的其他重要用例，如分割學習和垂直聯邦學習。最後，本書包括一些章節，專注於在現實企業環境中應用FL。
作者簡介
Heiko Ludwig is a Senior Manager, AI Platforms and a Principal Research Staff Member at IBM's Almaden Research Center in San Jose, CA. Heiko coordinates the Federated Learning program at IBM Research and oversees the Distributed AI research area. His research contributed to different products, including IBM's machine learning products. He is an ACM Distinguished Engineer and has more than 150 publications with more than 8000 citations. His technical work led to a number of technical awards by IBM and his numerous patents and patent applications received a designation as an IBM Master Inventor. Heiko is a co-editor in chief of the International Journal of Cooperative Information Systems and serves on the editorial boards of multiple journals. Heiko also serves regularly as program committee chair in conferences in the field. Heiko's wider interest is on large scale and cross-organizational AI systems and its related distributed systems, security and privacy research issues. Heiko received a doctorate in information systems from Otto-Friedrich-Universität Bamberg, Germany.
Nathalie Baracaldo leads the AI Security and Privacy Solutions team and is a Research Staff Member at IBM's Almaden Research Center in San Jose, CA. Nathalie is passionate about delivering machine learning solutions that are highly accurate, withstand adversarial attacks and protect data privacy. Nathalie has led her team to the design of IBM Federated Learning framework which is now part of the Watson Machine Learning product and continues to work on its expansion. In 2020, Nathalie received the IBM Master Inventor distinction for her contributions to the IBM Intellectual Property and innovation. Nathalie also received the 2021 Corporate Technical Recognition, one of the highest recognitions provided to IBMers for breakthrough technical achievements that have led to notable market and industry success for IBM. This recognition was awarded for Nathalie's contribution to the Trusted AI Initiative. Nathalie has been invited to give multiple talks on federated learning, its challenges and opportunities. Nathalie has received four best paper awards and published in top-tier conferences and journals, obtaining more than 1300 Google scholar citations. Nathalie's wider research interests include security and privacy, distributed systems and machine learning. Nathalie is also Associate Editor of the IEEE Transactions on Service Computing. Nathalie received her Ph.D. degree from the University of Pittsburgh in 2016.
Nathalie Baracaldo leads the AI Security and Privacy Solutions team and is a Research Staff Member at IBM's Almaden Research Center in San Jose, CA. Nathalie is passionate about delivering machine learning solutions that are highly accurate, withstand adversarial attacks and protect data privacy. Nathalie has led her team to the design of IBM Federated Learning framework which is now part of the Watson Machine Learning product and continues to work on its expansion. In 2020, Nathalie received the IBM Master Inventor distinction for her contributions to the IBM Intellectual Property and innovation. Nathalie also received the 2021 Corporate Technical Recognition, one of the highest recognitions provided to IBMers for breakthrough technical achievements that have led to notable market and industry success for IBM. This recognition was awarded for Nathalie's contribution to the Trusted AI Initiative. Nathalie has been invited to give multiple talks on federated learning, its challenges and opportunities. Nathalie has received four best paper awards and published in top-tier conferences and journals, obtaining more than 1300 Google scholar citations. Nathalie's wider research interests include security and privacy, distributed systems and machine learning. Nathalie is also Associate Editor of the IEEE Transactions on Service Computing. Nathalie received her Ph.D. degree from the University of Pittsburgh in 2016.
作者簡介(中文翻譯)
海科·路德維希（Heiko Ludwig）是IBM位於加州聖荷西的阿爾馬登研究中心的高級經理、人工智慧平台部門負責人及首席研究成員。海科負責協調IBM研究的聯邦學習計畫，並監督分散式人工智慧研究領域。他的研究對多個產品做出了貢獻，包括IBM的機器學習產品。海科是ACM傑出工程師，擁有超過150篇出版物，引用次數超過8000次。他的技術工作獲得了多項IBM的技術獎項，並且他的多項專利及專利申請獲得了IBM大師發明家的稱號。海科是《國際合作資訊系統期刊》的共同主編，並在多個期刊的編輯委員會中任職。海科也經常擔任該領域會議的程序委員會主席。海科的廣泛興趣在於大規模及跨組織的人工智慧系統及其相關的分散式系統、安全性和隱私研究問題。海科在德國巴姆貝格的奧托-弗里德里希大學獲得資訊系統博士學位。
娜塔莉·巴拉卡爾多（Nathalie Baracaldo）領導IBM位於加州聖荷西的阿爾馬登研究中心的人工智慧安全與隱私解決方案團隊，並擔任研究成員。娜塔莉熱衷於提供高準確度、能抵抗對抗性攻擊並保護數據隱私的機器學習解決方案。娜塔莉帶領她的團隊設計了IBM聯邦學習框架，該框架現在是Watson機器學習產品的一部分，並持續致力於其擴展。2020年，娜塔莉因其對IBM知識產權和創新的貢獻而獲得IBM大師發明家的榮譽。娜塔莉還於2021年獲得企業技術認可獎，這是IBM對突破性技術成就的最高認可之一，這些成就為IBM帶來了顯著的市場和行業成功。該認可是因娜塔莉對可信人工智慧倡議的貢獻而頒發的。娜塔莉曾多次受邀發表有關聯邦學習的演講，探討其挑戰和機會。娜塔莉獲得了四項最佳論文獎，並在頂級會議和期刊上發表，獲得超過1300次Google Scholar引用。娜塔莉的廣泛研究興趣包括安全性和隱私、分散式系統和機器學習。娜塔莉也是《IEEE服務計算學報》的副編輯。娜塔莉於2016年在匹茲堡大學獲得博士學位。
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