Federated Learning: A Comprehensive Overview of Methods and Applications
Ludwig, Heiko, Baracaldo, Nathalie
- 出版商: Springer
- 出版日期: 2023-07-09
- 售價: $6,580
- 貴賓價: 9.5 折 $6,251
- 語言: 英文
- 頁數: 534
- 裝訂: Quality Paper - also called trade paper
- ISBN: 3030968987
- ISBN-13: 9783030968984
海外代購書籍(需單獨結帳)
買這商品的人也買了...
-
Federated Learning Systems: Towards Next-Generation AI$6,950$6,603 -
Federated Learning with Python: Design and implement a federated learning system and develop applications using existing frameworks$1,880$1,786 -
Federated Learning for Wireless Networks$6,950$6,603 -
Federated Learning for Internet of Medical Things: Concepts, Paradigms, and Solutions$4,450$4,228 -
Federated Learning for Future Intelligent Wireless Networks$4,820$4,579 -
Handbook on Federated Learning: Advances, Applications and Opportunities$5,380$5,111 -
Federated AI for Real-World Business Scenarios$2,600$2,470 -
Security and Privacy in Federated Learning$6,300$5,985 -
Communication Efficient Federated Learning for Wireless Networks$6,580$6,251 -
Adversarial Multimedia Forensics$6,950$6,603 -
The Potential of Generative AI: Transforming Technology, Business and Art Through Innovative AI Applications$1,650$1,568 -
Federated Learning: Theory and Practice$4,480$4,256 -
Privacy-Preserving Machine Learning: A use-case-driven approach to building and protecting ML pipelines from privacy and security threats$1,820$1,729 -
Artificial Intelligence for Smart Manufacturing: Methods, Applications, and Challenges$5,640$5,358 -
Unleashing the Power of Data with Trusted AI: A guide for board members and executives$4,480$4,256 -
Security and Risk Analysis for Intelligent Edge Computing$6,970$6,622 -
Industrial Edge Computing: Architecture, Optimization and Applications$7,680$7,296 -
Mobile and Ubiquitous Systems: Computing, Networking and Services: 20th Eai International Conference, Mobiquitous 2023, Melbourne, Vic, Australia, Nov$4,010$3,810 -
LLM Model Security: Strategies, Best Practices, and Future Trends$1,090$1,036 -
LLM and Generative AI for Healthcare: A Comprehensive Guide$1,090$1,036 -
Generative AI Strategy and Implementation: Mastering Techniques and Advanced Applications$890$846 -
Generative AI Transformation: A Guide for Everyone$1,000$950 -
Generative AI for Manufacturing$2,320$2,204 -
Generative AI for Robotics and Autonomous Systems$2,300$2,185 -
Learn Python Generative AI: Journey from autoencoders to transformers to large language models (English Edition)$1,650$1,568
相關主題
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：方法和應用的全面概述》為研究人員和從業人員提供了對聯邦學習最重要的問題和方法的深入討論。聯邦學習（FL）是一種機器學習方法，其中訓練數據不由中央管理。數據由參與聯邦學習過程的數據方保留，並且不與任何其他實體共享。這使得聯邦學習成為解決將數據集中在一個中央存儲庫中可能出現的隱私、監管或實際問題的機器學習任務的越來越受歡迎的解決方案。
本書解釋了聯邦學習（FL）的最新研究進展和最新發展，從該領域的最初概念到首次應用和商業使用。為了獲得這個廣泛而深入的概述，領先的研究人員從聯邦學習的不同角度進行了討論：核心機器學習觀點、隱私和安全、分散系統以及特定應用領域。讀者將了解每個領域面臨的挑戰，它們如何相互關聯，以及它們如何通過最先進的方法解決。
在介紹中對聯邦學習基礎知識進行概述後，接下來的24章中，讀者將深入探討各種主題。第一部分討論了以聯邦方式解決不同機器學習任務的算法問題，以及如何高效、規模化和公平地進行訓練。另一部分則著重於提供清晰的指導，以選擇可以根據具體用例定制的隱私和安全解決方案，還有一部分考慮了聯邦學習過程將運行的系統的實際問題。本書還涵蓋了聯邦學習的其他重要應用案例，如分割學習和垂直聯邦學習。最後，本書還包括一些專注於在現實企業環境中應用FL的章節。
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)
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有所貢獻，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大師發明家。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。他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大師發明家的稱號。Nathalie還因其對可信AI倡議的貢獻而獲得2021年企業技術認可，這是IBM為突破性技術成就頒發給IBM員工的最高榮譽之一，該成就為IBM帶來了顯著的市場和行業成功。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。她於2016年在匹茲堡大學獲得博士學位。
