Study on Multi-Agent Interactive Learning Models Oriented Towards Dynamic Interactive Topologies

Machine learning has gradually become a dominant force in the field of artificial intelligence, achieving significant results in various domains such as computer vision, natural language processing, and multi-agent learning. However, as neural network architectures become increasingly complex and their application scenarios more extensive, fundamental issues such as poor robustness of deep learning models, inability to adapt to new tasks, and difficulties in scaling have become more apparent. This study incorporates the concept of collective intelligence into learning systems and proposes a multi-agent dynamic interactive learning model oriented towards dynamic interactive topologies, along with convergence conditions for the model. In the context of data privacy protection, the focus is on how multiple agents can emerge collective intelligence through dynamic cooperative interactions, resulting in more accurate learning outcomes. First, each site is treated as an independent agent, each possessing its own private data, which is also unbalanced across sites. Dynamic interaction rules are introduced to facilitate collaborative learning among multiple sites, and conditions for the convergence of these dynamic interaction rules are provided. Furthermore, to address practical challenges such as high communication costs, complex and variable communication environments, as well as issues related to data privacy and domain knowledge barriers, a multi-agent dynamic interactive learning model containing dynamic interaction rules is proposed. This model achieves decentralization, which can alleviate issues of data privacy, knowledge barriers, and security in the communication process. Experimental results demonstrate that the outcomes of multi-agent dynamic interactive learning surpass those of individual site learning.