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.

Federated multi-task learning (FMTL) has emerged as a promising framework for learning multiple tasks simultaneously with client-aware personalized models. While the majority of studies have focused on dealing with the non-independent and identically distributed (Non-IID) characteristics of client datasets, the issue of task heterogeneity has largely been overlooked. Dealing with task heterogeneity often requires complex models, making it impractical for federated learning in resource-constrained environments. In addition, the varying nature of these heterogeneous tasks introduces inductive biases, leading to interference during aggregation and potentially resulting in biased global models. To address these issues, we propose a hierarchical FMTL framework, referred to as $\mathtt{F}\mathtt{e}\mathtt{d}\mathtt{B}\mathtt{o}\mathtt{n}\mathtt{e}$, to facilitate the construction of large-scale models with improved generalization. $\mathtt{F}\mathtt{e}\mathtt{d}\mathtt{B}\mathtt{o}\mathtt{n}\mathtt{e}$ leverages server-client split learning and gradient projection to split the entire model into two components: 1) a large-scale general model (referred to as the general model) on the cloud server, and 2) multiple task-specific models (referred to as client models) on edge clients, accommodating devices with limited compute power. To enhance the robustness of the large-scale general model, we incorporate the conflicting gradient projection technique into $\mathtt{F}\mathtt{e}\mathtt{d}\mathtt{B}\mathtt{o}\mathtt{n}\mathtt{e}$ to rectify the skewed gradient direction caused by aggregating gradients from heterogeneous tasks. The proposed $\mathtt{F}\mathtt{e}\mathtt{d}\mathtt{B}\mathtt{o}\mathtt{n}\mathtt{e}$ framework is evaluated on three benchmark datasets and one real ophthalmic dataset. The comprehensive experiments demonstrate that $\mathtt{F}\mathtt{e}\mathtt{d}\mathtt{B}\mathtt{o}\mathtt{n}\mathtt{e}$ efficiently adapts to the heterogeneous local tasks of each client and outperforms existing federated learning algorithms in various dense prediction and classification tasks while utilizing off-the-shelf computational resources on the client side.