Intelligent deep federated learning model for enhancing security in internet of things enabled edge computing environment

In the present scenario, the Internet of Things (IoT) and edge computing technologies have been developing rapidly, foremost to the development of new tasks in security and privacy. Personal information and privacy leakage have become the main concerns in IoT edge computing surroundings. The promptly developing IoT-connected devices below an integrated Machine Learning (ML) method might threaten data confidentiality. The standard centralized ML-assisted methods have been challenging because they require vast numbers of data in a vital unit. Due to the rising distribution of information in many systems of linked devices, decentralized ML solutions have been required. Federated learning (FL) was proposed as an…

• KA
  King Abdulaziz University

  Award: GPIP-204-611-2024