AI-Native Federated Edge Intelligence for Ultra-Reliable, Low-Latency, and Resource-Efficient 6G-IoT Networks

Abstract

Mission-critical application is the Internet of Things (IoT), which is increasingly gaining popularity and will be even more popular in the future; ultra-reliability, low latency communication, scalability, efficient use of resources, and distributed intelligence should be supported by 6G. The works seek to improve intelligent 6G-IoT communication and have started adopting deep learning, distributed edge intelligence, and asynchronous federated learning and predictive resource orchestration. This long-term project involves the design and development of a new AI-Native Federated Edge Intelligence (AI-FEI) framework, which covers all the above features, and the new technologies of 6G, such as THz communication, reconfigurable intelligent surfaces (RIS), and integrated sensing and communication (ISC). Distributed IoT data privacy; proactive network-state prediction; dynamically optimizing use of the spectrum, transmission power, network slicing, and resources at the edge; and more are all possible features of the framework. Objective of reliability and energy awareness for the various missions' critical communication requirements for channel condition, traffic load, and device density. This long-term evaluation experiment takes into consideration latency, reliability, scaling aspects, energy efficiency, federated convergence, communication overhead, and computational performance.