An innovative Federated Learning paradigm for safeguarding privacy in IoT through a synergistic SDN-CLOUD Structure

Abstract

As global society increasingly transitions towards a comprehensive dependence on computational systems and digitization, the formulation of secure environments and connections emerges as one of the foremost challenges of this decade. The proliferation of threats confronting both individuals and organizations is escalating at an exponential rate, attributable to the burgeoning intricacy of contemporary networks and the ascendance of the Internet of Things. Insufficient collaboration engenders a scenario wherein identical attacks are executed against disparate organizations in rapid succession. The dissemination of cyber threat intelligence is frequently proposed as a remedy to this predicament; however, it introduces distinct constraints pertaining to Data Safeguarding, traceability, and Integrity. To address this issue, a novel distributed architecture is put forth to augment the protective measures within the IoT landscape. This solution is predicated on federated learning algorithms aimed at constructing a distributed, self-governing system able to Identify intrusion and breaches through a synergistic SDNCloud Structure. This structural paradigm leverages the potential of SDN and cloud computing for IoT endpoints to enable effective and adaptable data analysis while concurrently maintaining user confidentiality. The adoption of federated learning simplifies a non-centralized model learning workflow, reducing data exposure and infringements by keeping private information confined to the IoT endpoints. Consequently, this methodology not only bolsters security but also promotes interdevice cooperation to confront the challenges in an exponentially interconnected.