Federated Learning with R: Build Privacy-Preserving AI, Distributed Models, and Secure Machine Learning Systems Without Centralizing Data

Name: Federated Learning with R: Build Privacy-Preserving AI, Distributed Models, and Secure Machine Learning Systems Without Centralizing Data
Brand: Independently Published
SKU: 9798253828946
Price: 1741.0 INR
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ISBN13: 9798253828946
Binding: Paperback
Subject: N/A
Publisher: Independently Published
Publisher Imprint: Independently Published
Publication Date: March 26, 2026
Pages: 160
Original Price: USD 20.0
Language: English
Edition: N/A
Item Weight: 223 grams
BISAC Subject(s): Probability & Statistics / General

About The Book

FEDERATED LEARNING WITH R: Build Privacy-Preserving AI, Distributed Models, and Secure Machine Learning Systems Without Centralizing Data

Most machine learning systems assume one thing: data can be centralized.

In the real world, that assumption fails.

Data is fragmented across organizations, devices, and regions. Regulations restrict access. Privacy risks increase with every transfer. Traditional pipelines break not because the models are weak, but because the system design is wrong.

This book shows you how to fix that.

Instead of forcing data into a single location, you'll learn how to build models that move to the data-training across distributed environments without exposing sensitive information.

This is not a theoretical guide. It is a system-building manual.

Inside, you'll learn how to:

Build federated learning systems in R from the ground up
Train distributed models across multiple clients without sharing raw data
Implement robust aggregation strategies that don't collapse under real conditions
Handle non-IID data and stabilize training across heterogeneous environments
Apply differential privacy and secure aggregation to protect sensitive information
Reduce communication cost without sacrificing model performance
Design fault-tolerant systems that continue to function under failure
Deploy federated pipelines across cloud and edge environments
Evaluate models under real-world constraints, not ideal assumptions
Build a complete end-to-end federated learning system ready for production

This book is for:

Data scientists building privacy-aware systems
Machine learning engineers working with distributed data
R users moving into advanced AI system design
Organizations that cannot centralize data but still need high-performance models

What makes this book different:

It does not pretend federated learning is simple.
It shows you where systems break and how to fix them.

You won't just understand federated learning.
You'll build systems that actually work.

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