International Science and Technology Journal

Federated Learning (FL) is a machine learning framework that allows collaborative model training across multiple decentralized edge devices or servers while keeping the data local and private. This method eliminates the need to exchange sensitive data, enhancing privacy and security. FL leverages the distributed nature of data generation for more effective learning. The paper explores the foundational concepts of FL, focusing on how it enables collective learning without centralizing individual data. This is particularly important in sectors where data privacy is critical, such as healthcare and finance. It discusses the technical mechanisms of FL, including the algorithms for decentralized training, data aggregation techniques, and the role of a central server, when applicable. In FL, devices compute on local data and share only model updates or gradients, reducing the risk of exposing sensitive information. FL has diverse applications, such as enabling disease diagnosis models in healthcare without sharing patient data and enhancing privacy in mobile device personalization. However, FL also faces challenges, including issues with synchronization of updates, handling non-IID (independent and identically distributed) data across devices, and ensuring resistance to adversarial attacks. The paper addresses these challenges and examines strategies to overcome them, providing a comprehensive overview of FL's potential and its future applications [1, 2]................ Keywords: ...............Federated Learning, Privacy, Security, Decentralized, Machine Learning, Edge Devices