<p>Heterogeneous graphs are a widely adopted data representation paradigm owing to their ability to model multiple node types and complex relationships uniformly. However, due to privacy and ownership restrictions, semantic-rich heterogeneous graphs are often stored by multiple participants, each of whom holds a subgraph. Federated heterogeneous subgraph learning seeks to aggregate knowledge from structurally diverse, decentralized subgraphs. This collaborative approach trains a more robust graph model without exposing raw data, thus overcoming the challenges of modeling complex relationships. In practical applications, however, the incompleteness and heterogeneity of subgraphs often result in degraded performance of the federated heterogeneous subgraph learning model. The method based on structural complementarity for addressing structural incompleteness ignores the semantic rules and node roles that are unique to heterogeneous graphs. For heterogeneous subgraphs, the information compression method based on category labels loses key high-order topological semantics. Therefore, we propose a new federated learning framework, federated heterogeneous subgraph learning with meta-path-guided role mapping and condensation (FedPRC). Specifically, based on the principle of structural equivalence, we propose a meta-path-guided virtual node generation method. This method enables each client to effectively infer and complete missing semantic links locally. Based on the evaluation of the importance of subgraph structure, we design a meta-path-guided information-aware subgraph condensation method. This method uses the condensation subgraph as an effective carrier for cross-client knowledge alignment and communication, while retaining the key topology and high-order paths. Our experiments on multiple real-world graph datasets demonstrate that this framework significantly outperforms existing federated graph learning baseline models in node classification tasks, validating its effectiveness.</p>