Federated Learning for Enhancing Cybersecurity in IoT-Integrated 6G Networks: Challenges, Opportunities, and Future Directions

The emergence of sixth-generation (6G) networks portends groundbreaking changes including artificial intelligence-driven applications, ultra-low latency, and massive interaction. These capabilities are further strengthened by the gradual integration of the Internet of Things (IoT) into 6G networks, enabling promotes seamless interaction throughout billions of devices and facilitates intelligent and self-sufficient systems. But because 6G-IoT ecosystems have become disorganized as well as diverse, these advancements also bring alongside them substantial cybersecurity, scalability, and resource efficiency concerns. The insufficient capacity of conventional centralized safety mechanisms to adapt to these complexities demands innovative approaches. The implementation of IoT into 6G networks is explored in the current investigation, alongside the potential benefits of Federated Learning (FL) as an alternative for new problems. By facilitating decentralized model training across IoT devices, FL diminishes dependence on centralized data aggregation while safeguarding data privacy. Significant challenges are investigated, covering topics like integrating diversified IoT data, decreasing communication overhead, maintaining energy efficiency, and addressing scalability over billions of IoT-enabled devices. For the purpose of to further enhance 6G-IoT cybersecurity, FL integration using technologies like blockchain and quantum communication is also evaluated. Through the process of the formulation of a robust FL framework, this dissertation paves a path for fostering the development of resource-efficient, scalable, and privacy-preserving mechanisms that are effectively tailored to the dynamic circumstances of 6G-IoT networks.