Enhancing QoS in Edge Computing through Federated Layering Techniques: A Pathway to Resilient AI Lifelong Learning Systems

TL;DR

This paper presents a federated layering approach to improve QoS in edge computing by enhancing AI lifelong learning, efficiency, and privacy protection in resource-constrained environments, leveraging cloud-edge collaboration.

Contribution

It introduces Federated Layering Techniques (FLT) for small model collaboration, improving AI reasoning, learning efficiency, and privacy in edge computing environments.

Findings

Enhanced learning efficiency and reasoning accuracy.

Effective privacy protection for edge nodes.

Improved QoS in resource-limited edge environments.

Abstract

In the context of the rapidly evolving information technology landscape, marked by the advent of 6G communication networks, we face an increased data volume and complexity in network environments. This paper addresses these challenges by focusing on Quality of Service (QoS) in edge computing frameworks. We propose a novel approach to enhance QoS through the development of General Artificial Intelligence Lifelong Learning Systems, with a special emphasis on Federated Layering Techniques (FLT). Our work introduces a federated layering-based small model collaborative mechanism aimed at improving AI models' operational efficiency and response time in environments where resources are limited. This innovative method leverages the strengths of cloud and edge computing, incorporating a negotiation and debate mechanism among small AI models to enhance reasoning and decision-making processes. By integrating model layering techniques with privacy protection measures, our approach ensures the secure transmission of model parameters while maintaining high efficiency in learning and reasoning capabilities. The experimental results demonstrate that our strategy not only enhances learning efficiency and reasoning accuracy but also effectively protects the privacy of edge nodes. This presents a viable solution for achieving resilient large model lifelong learning systems, with a significant improvement in QoS for edge computing environments.