The Internet of Things (IoT) is rapidly expanding, connecting billions of devices worldwide and generating vast amounts of data. Traditional machine learning approaches often require centralized data collection, which raises privacy concerns, increases network congestion, and limits scalability. Federated learning offers a decentralized approach, enabling IoT devices to collaboratively learn a shared model while keeping … Read more
Recommender systems have revolutionized how businesses provide personalized experiences to users. From e-commerce platforms suggesting products to streaming services recommending movies, these systems are integral to user engagement and satisfaction. However, traditional recommender systems rely on centralized data collection, posing privacy concerns and regulatory challenges. Federated Learning (FL) emerges as a game-changing approach, allowing models … Read more
Federated learning (FL) is a decentralized approach to machine learning where models are trained across multiple devices or servers holding local data, without sharing raw data. While this approach enhances privacy and security, it introduces unique challenges, one of the most significant being class imbalance. Class imbalance occurs when the distribution of labels across clients … Read more
Federated learning (FL) is an innovative approach to machine learning that addresses privacy and data security concerns by enabling decentralized data processing. Instead of gathering raw data in a central server, FL trains algorithms across multiple decentralized devices holding local data samples, without exchanging them. This article explores the various benefits of federated learning, highlighting … Read more
