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Federated Neural Graph Databases (2402.14609v3)

Published 22 Feb 2024 in cs.LG, cs.AI, cs.CR, and cs.DB

Abstract: The increasing demand for large-scale LLMs has highlighted the importance of efficient data retrieval mechanisms. Neural graph databases (NGDBs) have emerged as a promising approach to storing and querying graph-structured data in neural space, enabling the retrieval of relevant information for LLMs. However, existing NGDBs are typically designed to operate on a single graph, limiting their ability to reason across multiple graphs. Furthermore, the lack of support for multi-source graph data in existing NGDBs hinders their ability to capture the complexity and diversity of real-world data. In many applications, data is distributed across multiple sources, and the ability to reason across these sources is crucial for making informed decisions. This limitation is particularly problematic when dealing with sensitive graph data, as directly sharing and aggregating such data poses significant privacy risks. As a result, many applications that rely on NGDBs are forced to choose between compromising data privacy or sacrificing the ability to reason across multiple graphs. To address these limitations, we propose Federated Neural Graph Database (FedNGDB), a novel framework that enables reasoning over multi-source graph-based data while preserving privacy. FedNGDB leverages federated learning to collaboratively learn graph representations across multiple sources, enriching relationships between entities and improving the overall quality of the graph data. Unlike existing methods, FedNGDB can handle complex graph structures and relationships, making it suitable for various downstream tasks.

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Authors (9)
  1. Qi Hu (33 papers)
  2. Weifeng Jiang (12 papers)
  3. Haoran Li (166 papers)
  4. Zihao Wang (216 papers)
  5. Jiaxin Bai (30 papers)
  6. Qianren Mao (13 papers)
  7. Yangqiu Song (196 papers)
  8. Lixin Fan (77 papers)
  9. Jianxin Li (128 papers)
Citations (1)
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