Secure Federated Learning With Fully Homomorphic Encryption for IoT Communications

The emergence of the Internet of Things (IoT) has revolutionized people’s daily lives, providing superior quality services in cognitive cities, healthcare, and smart buildings. However, smart buildings use heterogeneous networks. The massive number of interconnected IoT devices increases the possibility of IoT attacks, emphasizing the necessity of secure and privacy-preserving solutions. Federated learning (FL) has recently emerged as a promising machine learning (ML) paradigm for IoT networks to address these concerns. In FL, multiple devices collaborate to learn a global model without sharing their raw data. However, FL still faces privacy and security concerns due to the transmission of sensitive data…