Federated Learning : A Comprehensive Overview of Methods and Applications / edited by Heiko Ludwig, Nathalie Baracaldo.

Format:

Book

Contributor:

Ludwig, Heiko, Editor.

Baracaldo, Nathalie, Editor.

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

Language:

English

Subjects (All):

Artificial intelligence.

Machine learning.

Artificial Intelligence.

Machine Learning.

Local Subjects:

Artificial Intelligence.

Machine Learning.

Physical Description:

1 online resource (VI, 534 pages) : 134 illustrations, 117 illustrations in color.

Edition:

1st ed. 2022.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2022.

System Details:

text file PDF

Summary:

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. The first part addresses algorithmic questions of solving different machine learning tasks in a federated way and 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 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.

Contents:

Introduction to Federated Learning

Tree-Based Models for Federated Learning Systems

Semantic Vectorization: Text and Graph-Based Models

Personalization in Federated Learning

Personalized, Robust Federated Learning with Fed+

Communication-Efficient Distributed Optimization Algorithms

Communication-Efficient Model Fusion

Federated Learning and Fairness

Introduction to Federated Learning Systems

Local Training and Scalability of Federated Learning Systems

Straggler Management

Systems Bias in Federated Learning

Protecting Against Data Leakage in Federated Learning: What Approach Should You Choose?

Private Parameter Aggregation for Federated Learning

Data Leakage in Federated Learning

Security and Robustness in Federated Machine Learning

Dealing with Byzantine Threats to Neural Networks

Privacy-Preserving Vertical Federated Learning

Split Learning: A Resource Efficient Model and Data Parallel Approach for Distributed Deep Learning

Federated Learning for Collaborative Financial Crimes Detection

Federated Reinforcement Learning for Portfolio Management

Application of Federated Learning in Medical Imaging

Advancing Healthcare Solutions with Federated Learning

A Privacy-preserving Product Recommender System

Application of Federated Learning in Telecommunications and Edge Computing.

Other Format:

Printed edition:

ISBN:

978-3-030-96896-0

9783030968960

Access Restriction:

Restricted for use by site license.