Federated learning review: Fundamentals, enabling technologies, and future applications

Abstract

Federated Learning (FL) has been foundational in improving the performance of a wide range of applications since it was first introduced by Google. Some of the most prominent and commonly used FL-powered applications are Android’s Gboard for predictive text and Google Assistant. FL can be defined as a setting that makes on-device, collaborative Machine Learning possible. A wide range of literature has studied FL technical considerations, frameworks, and limitations with several works presenting a survey of the prominent literature on FL. However, prior surveys have focused on technical considerations and challenges of FL, and there has been a limitation in more recent work that presents a comprehensive overview of the status and future trends of FL in applications and markets. In this survey, we introduce the basic fundamentals of FL, describing its underlying technologies, architectures, system challenges, and privacy-preserving methods. More importantly, the contribution of this work is in scoping a wide variety of FL current applications and future trends in technology and markets today. We present a classification and clustering of literature progress in FL in application to technologies including Artificial Intelligence, Internet of Things, blockchain, Natural Language Processing, autonomous vehicles, and resource allocation, as well as in application to market use cases in domains of Data Science, healthcare, education, and industry. We discuss future open directions and challenges in FL within recommendation engines, autonomous vehicles, IoT, battery management, privacy, fairness, personalization, and the role of FL for governments and public sectors. By presenting a comprehensive review of the status and prospects of FL, this work serves as a reference point for researchers and practitioners to explore FL applications under a wide range of domains.