Collaborative Cloud–SDN architecture for IoT privacy-preserving based on federated learning

Abstract

As society increasingly relies on computers and automation, the challenge of developing secure applications, systems, and networks has become paramount. The complexity of modern networks and the proliferation of Internet of Things (IoT) devices have contributed to a surge in cyber threats facing individuals and organizations worldwide. Without effective collaboration, similar attacks can target multiple entities in rapid succession. While sharing cyber threat intelligence is often touted as a solution, privacy, trust, and traceability concerns persist. A novel distributed architecture is proposed to enhance IoT security to address these challenges. This solution relies on federated learning (FL) algorithms to establish a decentralized, autonomous system capable of detecting and characterizing attacks within a collaborative Cloud–SDN framework. Leveraging the strengths of Cloud computing and SDN, this architecture facilitates efficient and scalable data processing for IoT devices while safeguarding user privacy. By adopting FL, the model training process is decentralized, ensuring that sensitive data remains on the IoT devices, mitigating the risk of unauthorized access and data breaches.