This paper emphasizes the importance of efficient data retrieval mechanisms in response to the increasing demand for deep learning-based foundational models, and neural graph databases (NGDBs) are proposed as a solution. NGDBs utilize neural space to store and query graph-structured data, allowing LLMs to access accurate and contextually relevant information. However, existing NGDBs are limited to single-graph operations, have limited ability to infer across multiple distributed graphs, and do not support multi-source graph data, making it difficult to capture the complexity and diversity of real-world data. For sensitive graph data, direct sharing and aggregation pose significant privacy risks. Therefore, in this paper, we propose FedNGDB (Federated Neural Graph DataBase), a groundbreaking system framework that enables privacy-preserving inference on graph data from multiple sources. FedNGDB jointly learns graph representations from multiple sources using federated learning to enrich relationships between entities and improve the overall quality of graph data.