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Springer Federated Learning: Fundamentals and Advances - ML Guide

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Description

Federated Learning: Fundamentals and Advances is a comprehensive guide from Springer that explores the theoretical foundations and practical applications of federated learning within machine learning. Part of the Machine Learning: Foundations, Methodologies, and Applications series, this book covers essential concepts, advanced techniques, and real-world implementations for distributed machine learning systems. Ideal for researchers, practitioners, and students seeking to understand how federated learning enables collaborative model training across decentralised data sources whilst maintaining privacy and security. The text bridges the gap between foundational principles and cutting-edge developments in this rapidly evolving field.

Key Features

Federated Learning book published by Springer covering fundamental concepts and advanced methodologies in distributed machine learning.

Part of the Machine Learning: Foundations, Methodologies, and Applications series for structured learning progression.

Explores decentralised training approaches and privacy-preserving machine learning techniques for modern applications.

Suitable for researchers, engineers, and students developing expertise in federated systems and collaborative AI models.

Comprehensive coverage of both theoretical frameworks and practical implementation strategies for federated architectures.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
01 December 2023
Listed Since
04 November 2023

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