The Internet of Things (IoT) will be ripe for the deployment of novel machine learning algorithm for both network and application management. However, given the presence of massively distributed and private datasets, it is challenging to use classical centralized learning algorithms in the IoT. To overcome this challenge, federated learning can be a promising solution that enables on-device machine learning without the need to migrate the private end-user data to a central cloud. In federated learning, only learning model updates are transferred between end-devices and the aggregation server. Although federated learning can offer better privacy preservation than centralized machine learning, it has still privacy concerns. In this paper, first, we present the recent advances of federated learning towards enabling federated learning-powered IoT applications. A set of metrics such as sparsification, robustness, quantization, scalability, security, and privacy, is delineated in order to rigorously evaluate the recent advances. Second, we devise a taxonomy for federated learning over IoT networks. Finally, we present several open research challenges with their possible solutions.

中文翻译：

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物联网联合学习：最新进展、分类和开放挑战

物联网 (IoT) 已经成熟，可以为网络和应用程序管理部署新颖的机器学习算法。然而，鉴于存在大规模分布式和私有数据集，在物联网中使用经典的集中式学习算法具有挑战性。为了克服这一挑战，联合学习可以成为一种很有前景的解决方案，它可以实现设备上的机器学习，而无需将私有最终用户数据迁移到中央云。在联邦学习中，只有学习模型更新在终端设备和聚合服务器之间传输。尽管联邦学习可以提供比集中式机器学习更好的隐私保护，但它仍然存在隐私问题。在本文中，首先，我们介绍了联邦学习在启用联邦学习驱动的 IoT 应用程序方面的最新进展。描述了一组指标，例如稀疏化、鲁棒性、量化、可扩展性、安全性和隐私，以便严格评估最近的进展。其次，我们为物联网网络上的联合学习设计了一个分类法。最后，我们提出了几个开放的研究挑战及其可能的解决方案。