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Federated Learning
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.
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.
207 pages, ebook
Published December 19, 2019
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August 4, 2025
A concise yet comprehensive introduction to federated learning (FL), a distributed machine learning paradigm that enables collaborative model training without centralizing sensitive data. I would not mind covering basics of Federated AI, Zero-Trust metadata, and Dataspace concepts.
The book covers FL's core concepts, including its motivation, architecture, and key challenges like data heterogeneity, privacy, and communication efficiency. It explores algorithms (e.g., FedAvg), privacy-preserving techniques (e.g., differential privacy), and applications across domains like healthcare and IoT.
Clear explanations, formal treatment of FL frameworks, and practical insights into real-world implementations. However, some sections may feel technical for beginners, and the fast-evolving field means newer advancements are absent. Great for researchers and practitioners seeking a solid foundation in FL.
The book covers FL's core concepts, including its motivation, architecture, and key challenges like data heterogeneity, privacy, and communication efficiency. It explores algorithms (e.g., FedAvg), privacy-preserving techniques (e.g., differential privacy), and applications across domains like healthcare and IoT.
Clear explanations, formal treatment of FL frameworks, and practical insights into real-world implementations. However, some sections may feel technical for beginners, and the fast-evolving field means newer advancements are absent. Great for researchers and practitioners seeking a solid foundation in FL.
Displaying 1 of 1 review