A comprehensive review of federated learning: Methods, applications, and challenges in privacy-preserving collaborative model training

Abstract

Federated learning (FL) represents an advanced approach to tackling the issues linked with training machine learning (ML) models using distributed data while upholding privacy and security. It functions by enabling collaborative model training across a network of edge devices or servers, all without the need to transfer raw data. In place of sending data to a central server, which could potentially compromise privacy, federated learning empowers individual devices to conduct local training on their respective data. These updates are subsequently combined to develop an enhanced global model over multiple iteration. Additionally, as artificial intelligence (AI) becomes pervasive in novel application areas, concerns about the privacy of data and users are on the rise. This article offers an in-depth analysis of the advancements in FL, covering a wide array of topics including methodologies, applications, and challenges. By sidestepping the need to transfer raw data and instead focusing on sharing model updates or gradients, FL ensures the preservation of privacy and the efficient utilization of resources. Additionally, we investigate the diverse spectrum of application domains where FL holds significance. Instances encompass healthcare, finance, agriculture, education, Internet of Things (IoT), and industrial processes, all benefiting from the capacity of federated learning to harness data from decentralized sources without compromising data security. This article addresses complications such as model diversity, Non-IID (independent and identically distributed) data distribution, communication complexities, and security vulnerabilities. Furthermore, we discuss considerations related to regulatory compliance and ethics within the context of federated learning, particularly as data privacy regulations intensify.

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