Post- Edge-AI implementation for processing ultralow latency in 6G ecosystems for intelligent decision-making

Smart decision-making for applications like autonomous, smart city, and industrial automation is only achieved by using ultra-low latency processing, thanks to the deployment of 6G networks. Coupled with AI at the periphery through edge computing, edge AI enhances the speed and efficiency of processing data on the periphery of the network.In contrast, existing AI techniques based on the cloud suffer from privacy due to centralized processing, high bandwidth consumption, and high latency. Conventional methods are not suitable for 6G environments because of these limitations, which hinder real-time decision-making.We present a Federated Learning with Multi-Access Edge Computing (FL-MEC) design that lessens the need for cloud computing and decouples training of AI models across edge nodes to address these issues. This approach enhances data privacy, lowers transmission overhead, and maximizes use of network resources in order to accomplish extremely low latency.Smart grid control, healthcare based on IoT, and autonomous vehicles are few of the innovative 6G- based applications using the proposed approach. Adaptive learning, real-time response, and end-to-end security are all ensured without sacrificing efficiency.The proposed framework is a great fit for future 6G ecosystems, as experimental results indicate that it enhances energy economy by 96.3%, reduces latency by 38.9%, and enhances decision accuracy by 97.63% compared to state-of-the-art cloud-based AI methods.