Machine Unlearning: Theory and Applications in Networking
暫譯: 機器遺忘：網路中的理論與應用

Xu, Jie, Jia, Xiaohua

Machine Unlearning: Theory and Applications in Networking
  • 出版商: Springer
  • 出版日期: 2026-08-19
  • 售價: $6,910
  • 貴賓價: 9.5 折 $6,564
  • 語言: 英文
  • 頁數: 206
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 3032309778
  • ISBN-13: 9783032309778
  • 相關分類: Machine Learning

    • 海外代購書籍(需單獨結帳)

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商品描述

This book is a comprehensive guide to machine unlearning, covering both theoretical foundations and practical algorithms. The first part develops data influence measurement methods, including real-time and time-varying valuation frameworks. The second part presents exact and approximate unlearning approaches for large-scale models, with a focus on wireless and networked systems.

As AI models face growing demands to remove specific training data due to privacy regulations, security threats, or data quality concerns, machine unlearning has emerged as an efficient alternative to costly full retraining. This challenge is particularly critical in networked environments where user-generated data is continuously produced at scale.

This book is designed for researchers and graduate students in computer science, AI, and data privacy who seek to understand machine unlearning and explore open research challenges. It is also useful to industry practitioners in telecommunications and edge computing who need practical solutions for data removal and privacy compliance. By covering both current methods and future directions such as federated unlearning and unlearning for foundation models, this book provides a clear roadmap for advancing machine unlearning and building more trustworthy and adaptable AI systems.

商品描述(中文翻譯)

這本書是關於機器遺忘的綜合指南，涵蓋了理論基礎和實用算法。第一部分發展了數據影響測量方法，包括實時和時間變化的評估框架。第二部分介紹了針對大規模模型的精確和近似遺忘方法，重點關注無線和網絡系統。

隨著人工智慧模型面臨越來越多的需求，要求因隱私法規、安全威脅或數據質量問題而移除特定的訓練數據，機器遺忘已成為一種高效的替代方案，取代成本高昂的全面重訓練。這一挑戰在用戶生成數據持續大規模產生的網絡環境中特別關鍵。

本書旨在為計算機科學、人工智慧和數據隱私領域的研究人員和研究生提供理解機器遺忘的機會，並探索開放的研究挑戰。對於需要實用解決方案以進行數據移除和隱私合規的電信和邊緣計算行業從業者來說，本書也非常有用。通過涵蓋當前方法和未來方向，如聯邦遺忘和基礎模型的遺忘，本書提供了一個清晰的路線圖，以推進機器遺忘並建立更值得信賴和可適應的人工智慧系統。

作者簡介

Jie Xu is currently a Postdoctoral Fellow in the Department of Computer Science at City University of Hong Kong. She received her B.Eng. degree in Information Security and B.A. degree in Communication from the University of Science and Technology of China (USTC) in 2017, M.Eng. degree in Electronics and Communication Engineering from USTC in 2020, and Ph.D. degree in Computer Science from City University of Hong Kong in 2024. She is a recipient of the CityU Presidential Ph.D. Scholarship and the Best Paper Runner-Up Award at IEEE MASS 2018. Her research interests include trustworthy artificial intelligence, distributed systems, and data privacy. Her work has appeared in leading venues including ICML, ACL, and ICLR.

Xiaohua Jia is an IEEE Fellow and ACM Fellow. He is currently a Chair Professor in the Department of Computer Science at City University of Hong Kong and Director of the Center of Decentralized Trust Computing (CDTC). He received his BSc and MSc in Computer Science from the University of Science and Technology of China in 1984 and 1986, respectively, and his DSc degree in Information Science from the University of Tokyo in 1991. His research interests include distributed systems, data privacy and security, and cloud computing. He serves as an Editor for IEEE Transactions on Computers and has chaired major conferences including IEEE ICDCS 2023, ACM ICN 2019 and ACM MobiHoc 2008.

作者簡介(中文翻譯)

徐杰目前是香港城市大學計算機科學系的博士後研究員。她於2017年在中國科學技術大學（USTC）獲得資訊安全的工學學士學位及傳播學的文學學士學位，於2020年獲得USTC電子與通信工程的工學碩士學位，並於2024年在香港城市大學獲得計算機科學的博士學位。她是CityU總統博士獎學金的獲得者，並在IEEE MASS 2018中獲得最佳論文亞軍獎。她的研究興趣包括可信的人工智慧、分散式系統和數據隱私。她的研究成果已發表於包括ICML、ACL和ICLR等頂尖會議。

賈小華是IEEE Fellow和ACM Fellow。他目前是香港城市大學計算機科學系的講座教授及去中心化信任計算中心（CDTC）的主任。他於1984年和1986年分別在中國科學技術大學獲得計算機科學的學士和碩士學位，並於1991年在東京大學獲得資訊科學的博士學位。他的研究興趣包括分散式系統、數據隱私與安全以及雲計算。他擔任IEEE Transactions on Computers的編輯，並曾主辦包括IEEE ICDCS 2023、ACM ICN 2019和ACM MobiHoc 2008等重要會議。

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