Distributed and Collaborative Lightweight Edge Federated Learning for IoT Zombie Devices Detection

Abstract

The fast development of artificial intelligence and Internet of Things (IoT) technologies has enabled various applications of smart cities, e.g., smart monitoring and surveillance. However, vulnerabilities of IoT devices bring new threats to the security of smart cities. To identify ubiquitous IoT botnet attacks, a distributed and collaborative lightweight edge federated learning model for IoT zombie devices detection is proposed, named FIOT. To reduce computational complexity and enhance the adaptability to new attack environment at the network edge, FIOT is designed in a lightweight manner based on feature dimensionality reduction and transfer learning. Three IoT botnet datasets are used to validate the effectiveness of the proposed FIOT. Experimental results show that FIOT has an accuracy loss of less than 3% in terms of F1 value compared to the centralized learning, but the training time of FIOT is only 14.3% of that of centralized learning. While ensuring high detection accuracy, the number of parameters of FIOT is compressed to 37.58% of the comparison method.