Submission Deadline: 30 April 2023 (closed)
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
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