MoFedNet: Semantic Link is All Models Need

The future of artificial intelligence (AI) systems lies in large-scale, heterogeneous agent collaboration to solve increasingly diverse and complex tasks. This collaborative paradigm promises greater flexibility and scalability but brings a fundamental challenge: how to balance collaboration efficiency with task quality. Existing Model Context Protocol (MCP) and Agent-to-Agent (A2A) architectures struggle to achieve this trade-off, facing either scalability bottlenecks or unstable coordination. To address these issues, we propose MoFedNet (Model Federalization Network), a novel semantic link-guided model collaboration system that integrates disparate models into a federated collective. MoFedNet features a dual-layer architecture with centralized monitoring and decentralized collaboration. Its core semantic link (SL) mechanism enables scalable, efficient, and dynamic collaboration among heterogeneous models while supporting intelligent task orchestration and network evolution. We enhance MoFedNet with three key modules. The Link-to-Link Semantic Protocol (L2L) organizes interactions over the hypergraph. The Contextual Memory Enhanced Retrieval Module (CoMER) introduces structured memory modules to store and retrieve representations across abstraction levels. The Evolutionary Local-global Optimization Module (EvoLOM) drives continuous improvement by accumulating memory and optimizing the hypergraph. We propose a novel quality-efficiency (QE) index to evaluate system performance. In a large-scale generic task simulation with over 15 000 nodes, MoFedNet achieves a QE score of 0.74, significantly outperforming the MCP and A2A approaches. Furthermore, when applied to a real-world medical task, MoFedNet attains a QE of 0.63, demonstrating clear superiority over existing multi-agent methods alongside stable temporal and spatial complexity.