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

arXiv 2507.20444 ↗First posted 28 Jul 2025cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 16 references that are in the map. First posted on arXiv 28 Jul 2025.

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References · 5

  1. 2018D"IoT: A Federated Self-learning Anomaly Detection System for IoTcited by 89
  2. 2021Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challengescited by 76
  3. 2022Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directionscited by 35
  4. 2023Big AI Models for 6G Wireless Networks: Opportunities, Challenges, and Research Directionscited by 32
  5. 2021Towards 6G Internet of Things: Recent Advances, Use Cases, and Open Challengescited by 4

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