In traditional machine learning, the data from different sources has to be moved to a central location, where the machine learning models will get trained to understand the patterns existing in the data. Due to the increased applications of Internet of Things (IoT)-based applications, sensitive data collected by IoT devices is being transferred to the cloud for training machine learning algorithms to understand the patterns in the data. The sensitivity of these data can attract malicious users into hacking attempts. The solution to this problem is a machine learning model which gets trained at the source of the data, instead of being trained at central locations like the cloud. Federated Learning is a recent advancement of machine learning, where, instead of moving the data to the central cloud, the machine learning model itself is moved to the source of the data. Hence, Federated Learning has the potential to solve several issues regarding cyber security in IoT based applications.


Full submissions of accepted abstracts should be completed by November 19th, 2021. Authors that require more time should contact communities@peerj.com to request an extension.

Topics include:

Federated Learning For Intrusion Detection In IoT
Federated Learning With Edge Computing For Cybersecurity In IoT
Federated Learning With Blockchain For Cybersecurity In IoT
Federated Learning For Security And Privacy In IoT
Federated Learning For Anomaly Detection In IoT
Big Data Analytics With Federated Learning For Cybersecurity In IoT
Federated Learning For Smart Grids
Federated Learning For Industrial IoT
Federated Learning For Energy Efficiency In IoT
Federated Learning For Privacy Preservation Of The Users In Social Media Apps
Federated Learning For 5G And Beyond

EDITORS
Dr Mamoun Alazab (Charles Darwin University) and Dr Thippa Reddy Gadekallu (Vellore Institute of Technology)