Table of Content

Federated Learning Algorithms, Approaches, and Systems for Internet of Things

Submission Deadline: 31 December 2022 Submit to Special Issue

Guest Editors

Prof. Mu Zhou, Chongqing University of Posts and Telecommunications, China
Prof. Ying-Ren Chien, National Ilan University, Taiwan
Prof. Xin Liu, Dalian University of Technology, China


This special issue focuses on algorithms, approaches, and systems based on federated learning for the Internet of Things (IoT) in the smart industry, smart transportation, and smart healthcare, etc. With the development of the IoT, it has ushered in the explosive growth of data and the rapid development of machine learning. However, this creates data security and privacy issues while providing convenient services, and federated learning comes into being. Federated learning is essentially a distributed machine learning technique, or machine learning framework. The goal of federated learning is to achieve joint modeling and improve the effect of Artificial Intelligence (AI) models on the basis of ensuring data privacy security and legal compliance. On the premise of ensuring information security, terminal data privacy, and personal data privacy during data exchange, federated learning can perform high-efficiency machine learning among multiple computing nodes, and is expected to become the basis of the next generation of artificial intelligence collaborative algorithms and collaborative networks. This special issue aims to explore federated learning algorithms, approaches, and systems for the IoT, and provide high-quality IoT services while protecting data privacy and information security. Potential topics include but are not limited to the following:

— Federated learning for IoT data sharing, offloading, and caching

— Federated learning for IoT attack detection

— Federated learning for IoT mobile crowd-sensing

— Federated learning for IoT localization and tracking

— Federated learning for IoT security and privacy

— Federated learning for data-driven IoT systems

— Federated learning for IoT on the blockchain

— The combination of federated learning and distributed machine learning

— Applications of federal learning in city and industry intelligentization


Federated learning; Artificial Intelligence; Internet of Things; wireless networks; machine learning; data privacy; information security

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