Federated Learning for Internet of Things: Applications, Challenges, and Opportunities
This is an incremental review that discusses opportunities and challenges of FL for IoT applications, targeting researchers and practitioners in IoT and machine learning.
The paper addresses the challenges of centralized learning for IoT, such as high communication costs and privacy concerns, by proposing Federated Learning (FL) as an alternative to enable collaborative model training without centralizing data.
Billions of IoT devices will be deployed in the near future, taking advantage of faster Internet speed and the possibility of orders of magnitude more endpoints brought by 5G/6G. With the growth of IoT devices, vast quantities of data that may contain users' private information will be generated. The high communication and storage costs, mixed with privacy concerns, will increasingly challenge the traditional ecosystem of centralized over-the-cloud learning and processing for IoT platforms. Federated Learning (FL) has emerged as the most promising alternative approach to this problem. In FL, training data-driven machine learning models is an act of collaboration between multiple clients without requiring the data to be brought to a central point, hence alleviating communication and storage costs and providing a great degree of user-level privacy. However, there are still some challenges existing in the real FL system implementation on IoT networks. In this paper, we will discuss the opportunities and challenges of FL in IoT platforms, as well as how it can enable diverse IoT applications. In particular, we identify and discuss seven critical challenges of FL in IoT platforms and highlight some recent promising approaches towards addressing them.