Federated Learning: A Comprehensive Overview of Methods and Applications
Ludwig, Heiko, Baracaldo, Nathalie
- 出版商: Springer
- 出版日期: 2022-07-08
- 售價: $6,380
- 貴賓價: 9.5 折 $6,061
- 語言: 英文
- 頁數: 392
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 3030968952
- ISBN-13: 9783030968953
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.
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.
Heiko Ludwig是IBM Almaden Research Center的高級經理、AI平台負責人和首席研究員。他負責協調IBM Research的聯邦學習計劃並監督分散式AI研究領域。他的研究對不同的產品做出了貢獻，包括IBM的機器學習產品。他是ACM杰出工程師，擁有150多篇論文，引用次數超過8000次。他的技術工作為IBM贏得了多項技術獎，他的許多專利和專利申請被評為IBM的主要發明家。Heiko是《國際合作信息系統期刊》的聯合主編，並擔任多個期刊的編輯委員會成員。他還經常擔任領域內會議的程序委員會主席。Heiko對大規模和跨組織的AI系統及其相關的分散式系統、安全和隱私研究問題感興趣。Heiko在德國巴姆貝格的奧托-弗里德里希大學獲得了信息系統博士學位。
Nathalie Baracaldo是IBM Almaden Research Center的AI安全和隱私解決方案團隊負責人和研究員。Nathalie熱衷於提供高度準確、能抵禦對抗性攻擊並保護數據隱私的機器學習解決方案。Nathalie帶領團隊設計了IBM聯邦學習框架，該框架現已成為Watson Machine Learning產品的一部分，並繼續擴展。2020年，Nathalie因其對IBM知識產權和創新的貢獻而獲得IBM主要發明家的稱號。Nathalie還獲得了2021年的企業技術認可，這是IBM為突破性技術成就頒發給IBM員工的最高榮譽之一，該認可是為了表彰Nathalie對可信AI倡議的貢獻。Nathalie曾應邀就聯邦學習、其挑戰和機遇發表多次演講。Nathalie獲得了四項最佳論文獎，並在頂級會議和期刊上發表，Google學者引用次數超過1300次。Nathalie的更廣泛研究興趣包括安全和隱私、分散式系統和機器學習。Nathalie還擔任IEEE服務計算交易的副編輯。Nathalie於2016年在匹茲堡大學獲得博士學位。