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Federated Learning - Research Book

Federated Learning - Research Book

Interesting Research Book in Federated Learning

Author(s) Name:  Qiang Yang, Yang Liu, Yong Cheng, Yan Kang, Tianjian Chen, Han Yu

About the Book:

   How is it possible to allow multiple data owners to collaboratively train and use a shared prediction model while keeping all the local training data private? Traditional machine learning approaches need to combine all data at one location, typically a data center, which may very well violate the laws on user privacy and data confidentiality. Today, many parts of the world demand that technology companies treat user data carefully according to user-privacy laws. The European Union-s General Data Protection Regulation (GDPR) is a prime example. In this book, we describe how federated machine learning addresses this problem with novel solutions combining distributed machine learning, cryptography and security, and incentive mechanism design based on economic principles and game theory. We explain different types of privacy-preserving machine learning solutions and their technological backgrounds, and highlight some representative practical use cases. We show how federated learning can become the foundation of next-generation machine learning that caters to technological and societal needs for responsible AI development and application.

Table of Contents

  • Introduction
  • Background
  • Distributed Machine Learning
  • Horizontal Federated Learning
  • Vertical Federated Learning
  • Federated Transfer Learning
  • Incentive Mechanism Design for Federated Learning
  • Federated Learning for Vision, Language, and Recommendation
  • Federated Reinforcement Learning
  • Selected Applications
  • Summary and Outlook

    • ISBN:  978-1681736976

    Publisher:  Morgan & Claypool Publishers

    Year of Publication:  2019

    Book Link:  Home Page Url