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.

自 Google 首次推出联邦学习 (FL) 以来，它一直是提高各种应用程序性能的基础。一些最突出和最常用的基于 FL 的应用程序是用于预测文本的 Android Gboard 和 Google Assistant。FL 可以定义为使设备上的协作机器学习成为可能的设置。大量文献研究了 FL 技术考虑、框架和局限性，其中几部作品对 FL 的著名文献进行了调查。然而，之前的调查主要关注 FL 的技术考虑和挑战，最近的工作存在局限性，无法全面概述 FL 在应用和市场中的现状和未来趋势。在本次调查中，我们介绍了 FL 的基本原理，描述其底层技术、架构、系统挑战和隐私保护方法。更重要的是，这项工作的贡献在于确定了广泛的 FL 当前应用以及当今技术和市场的未来趋势。我们介绍了 FL 在人工智能、物联网、区块链、自然语言处理、自动驾驶汽车和资源分配等技术中的应用，以及在数据科学领域的市场用例应用中的文献进展的分类和聚类、医疗保健、教育和工业。我们讨论了 FL 在推荐引擎、自动驾驶汽车、物联网、电池管理、隐私、公平、个性化以及 FL 对政府和公共部门的作用中未来的开放方向和挑战。