Data serving as a basic strategic resource is playing an important underpinning role in joint combat system. The proper management and efficient use of data are an important driving force in promoting the overall transformation of combat capability and the deep transformation of combat style. In order to realize the information interconnection between different combat systems, a multi-source heterogeneous network data fusion method is proposed based on the federated learning. In view of the security and heterogeneity of multi-source data, the conditional generation adversarial network is utilized for extracting local knowledge and global distribution, and integrating data information. In combination with the local teacher model-global model architecture, the local model knowledge is transferred by distillation of knowledge without data, the heterogeneous network is fused, and the global model is refined to realize safe and highquality information interaction between different systems, providing technical support for the construction of intelligent command information system. The experimental results show that the proposed method is feasible on structural data sets and image data sets, and the overall accuracy can be more than 80%.