Requirements: .ePUB, .PDF reader, 12.8 MB
Overview: The effectiveness of Federated Learning (FL) in high‑performance information systems and informatics‑based solutions for addressing current information support requirements is demonstrated in this book. To address heterogeneity challenges in Internet of Things (IoT) contexts, Federated Learning for Smart Communication using IoT Application analyses the development of personalized Federated Learning algorithms capable of mitigating the detrimental consequences of heterogeneity in several dimensions. It includes case studies of IoT‑based human activity recognition to show the efficacy of personalized Federated Learning for intelligent IoT applications. Federated Learning (FL) is leading the way in revolutionary developments in Machine Learning, transforming the traditional field of centralized model training. Fundamentally, FL is a novel technique that enables a network of dispersed devices to jointly train Machine Learning models. FL prioritizes privacy above central processing of raw data, as is the case with traditional approaches. Individual devices—such as cellphones, edge devices, or other endpoints—contribute to model training under this novel paradigm without disclosing private information. We will explore the fundamentals of FL, its uses, and its potential to revolutionize the ever-evolving field of Artificial Intelligence (AI) as we delve into its depths. This book is recommended for anyone interested in Federated Learning‑based intelligent algorithms for smart communications.
Genre: Non-Fiction > Tech & Devices
Download Instructions:
https://ouo.io/9JgeE1
https://ouo.io/zL74EuW.
