Distributed and federated learning significantly enhances the productivity of data scientists by enabling them to run parallel experiments across various devices, including GPUs, TPUs, and servers. These approaches reduce training time by distributing the training of a single network across multiple devices, resulting in shorter wait times for model training. Consequently, distributed machine learning allows professionals to decrease both model training and inference times drastically.
This book reviews advanced algorithms and applications that address research gaps across diverse domains such as healthcare, agriculture, retail, education, marketing, sales, etc. The book strives to provide robust literature support while exploring advanced algorithms designed to improve distributed deep learning and create scalable intelligent systems. Targeted at data scientists, machine and deep learning engineers, and practitioners in both academia and business, it offers innovative methods for constructing federated learning models with reduced training times. It provides a comprehensive range of tools for designing networked machine and deep learning models, tailored for advanced research across multiple domains. The primary focus is on developing innovative structures for deep learning models through the implementation of model parallelism and data parallelism, yielding more accurate results while minimizing training time and complexity. This book seeks to introduce advanced methods for distributed deep learning platforms, focusing on various issues and challenges while improving performance using federated learning algorithms and their applications to establish best practices in intelligent systems.