Harnessing Small AI Model Collaboration and Debate Mechanisms in 6G Networks: Distributed Architectures and Layered Privacy Protection

In the context of 6G communication networks, large communication models emerge as the core application for edge communication networks. They hold immense application potential in areas like intelligent connected vehicles, collaborative robots, and holographic communications. However, practical implementations of these applications face challenges such as unstable learning environments, resource constraints, information theft, and heterogeneity of edge devices. This paper delves into the distributed deployment of large communication models under resource-constrained nodes and introduces a distributed architecture based on cloud-edge collaboration. Crucially, we introduce a negotiation and debate mechanism between small AI models. This mechanism adopts innovative strategies, enabling it to match or even surpass the capabilities of large language models in terms of inference and accuracy. Furthermore, addressing the data confidentiality issues of large model parameter transmissions, this study proposes a secure federated learning mechanism based on privacy computing. Combining novel model layering technology with privacy protection measures, it aims to enhance the security of model parameter transmissions. Experimental results confirm that while improving learning efficiency and inference accuracy, this strategy successfully ensures the privacy security of edge nodes, offering a practical solution for future privacy protection.