Machine Learning ( ML ) has demonstrated great promise in various fields, e.g., self-driving, smart city, which are fundamentally altering the way individuals and organizations live, work, and interact. Traditional centralized learning frameworks require uploading all training data from different sources to a remote data server, which incurs significant communication overhead, service latency, and privacy issues. To further extend the frontiers of the learning paradigm, a new learning concept, namely, Edge Learning ( EL ) is emerging. It is complementary to the cloud-based methods for big data analytics by enabling distributed edge nodes to cooperatively training models and conduct inferences with their locally cached data. To explore the new characteristics and potential prospects of EL, we conduct a comprehensive survey of the recent research efforts on EL. Specifically, we first introduce the background and motivation. We then discuss the challenging issues in EL from the aspects of data, computation, and communication. Furthermore, we provide an overview of the enabling technologies for EL, including model training, inference, security guarantee, privacy protection, and incentive mechanism. Finally, we discuss future research opportunities on EL. We believe that this survey will provide a comprehensive overview of EL and stimulate fruitful future research in this field.

中文翻译：

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边缘学习

机器学习(机器学习) 在自动驾驶、智慧城市等各个领域都展现出了巨大的潜力，这些领域正在从根本上改变个人和组织的生活、工作和互动方式。传统的集中式学习框架需要将来自不同来源的所有训练数据上传到远程数据服务器，这会产生巨大的通信开销、服务延迟和隐私问题。为了进一步扩展学习范式的前沿，一个新的学习概念，即，边缘学习(埃尔) 正在出现。它通过使分布式边缘节点能够协同训练模型并使用其本地缓存的数据进行推理，来补充基于云的大数据分析方法。为了探索EL的新特性和潜在前景，我们对EL最近的研究工作进行了全面调查。具体来说，我们首先介绍背景和动机。然后，我们从数据、计算和通信方面讨论 EL 中的挑战性问题。此外，我们还概述了 EL 的使能技术，包括模型训练、推理、安全保证、隐私保护和激励机制。最后，我们讨论了未来关于 EL 的研究机会。