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“Federated Learning – Hope and Scope” explores the concept of federated learning (FL) as a decentralized approach to machine learning. It addresses challenges of data privacy and security, particularly in sectors like smart health and fintech. The document explains how FL enables collaborative training of models across multiple devices or institutions without sharing sensitive data. It discusses different types of FL, such as Horizontal, Vertical, and Transfer Learning, and highlights the potential of FL to enhance data privacy while improving machine learning model accuracy. The study emphasizes FL’s promise in creating privacy-preserving solutions for healthcare and financial services, which are traditionally constrained by data sharing regulations.
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Cybernetic Artificial Intelligence – A New Paradigm in Modeling Complex Systems As humanity faces unprecedented challenges in global health, environment,
The Challenges and Opportunities for Geothermal Energy in Indonesia Indonesia, the world’s second-largest producer of geothermal energy, sits at the
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Introduction The quality and safety of grain and its derived products depend significantly on effective moisture control throughout their lifecycle—from
Introduction The digital age has redefined the battlefield, with cyberspace emerging as a critical domain in modern conflicts. Cyber Threat
Introduction In the quest for higher efficiency and productivity, boilers undergo significant modifications. One notable example is the reconstruction of
Introduction In a world where dynamic environments require sophisticated monitoring and analysis, 3D LiDAR (Light Detection and Ranging) technology is