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FedE: Embedding Knowledge Graphs in Federated Setting

arXiv 2010.12882 ↗First posted 24 Oct 2020cs.CLcited by 46

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Abstract (arXiv)

Knowledge graphs (KGs) consisting of triples are always incomplete, so it's important to do Knowledge Graph Completion (KGC) by predicting missing triples. Multi-Source KG is a common situation in real KG applications which can be viewed as a set of related individual KGs where different KGs contains relations of different aspects of entities. It's intuitive that, for each individual KG, its completion could be greatly contributed by the triples defined and labeled in other ones. However, because of the data privacy and sensitivity, a set of relevant knowledge graphs cannot complement each other's KGC by just collecting data from different knowledge graphs together. Therefore, in this paper, we introduce federated setting to keep their privacy without triple transferring between KGs and apply it in embedding knowledge graph, a typical method which have proven effective for KGC in the past decade. We propose a Federated Knowledge Graph Embedding framework FedE, focusing on learning knowledge graph embeddings by aggregating locally-computed updates. Finally, we conduct extensive experiments on datasets derived from KGE benchmark datasets and results show the effectiveness of our proposed FedE.

Key facts. Cited by 46 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Oct 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 7; 2022: 8; 2023: 8; 2024: 11; 2025: 5; 2026: 7.

46 papers.

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  1. 2021Federated Graph Classification over Non-IID Graphscited by 113
  2. 2022Federated Learning on Non-IID Graphs via Structural Knowledge Sharingcited by 88
  3. 2021From Distributed Machine Learning to Federated Learning: A Surveycited by 65
  4. 2022Federated Graph Neural Networks: Overview, Techniques and Challengescited by 52
  5. 2021Differentially Private Federated Knowledge Graphs Embeddingcited by 45
  6. 2023Heterogeneous Federated Knowledge Graph Embedding Learning and Unlearningcited by 39
  7. 2021Federated Graph Learning -- A Position Papercited by 34
  8. 2021Meta-Knowledge Transfer for Inductive Knowledge Graph Embeddingcited by 31
  9. 2021A Vertical Federated Learning Framework for Graph Convolutional Networkcited by 20
  10. 2021Federated Learning of Molecular Properties with Graph Neural Networks in a Heterogeneous Settingcited by 19
  11. 2022Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregationcited by 18
  12. 2022Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the Federated Settingcited by 15
34 more
  1. 2022Federated Graph Machine Learning: A Survey of Concepts, Techniques, and Applicationscited by 14
  2. 2024Deep Efficient Private Neighbor Generation for Subgraph Federated Learningcited by 14
  3. 2024Federated Graph Learning with Structure Proxy Alignmentcited by 14
  4. 2024Safety in Graph Machine Learning: Threats and Safeguardscited by 11
  5. 2023Entity-Agnostic Representation Learning for Parameter-Efficient Knowledge Graph Embeddingcited by 8
  6. 2023Advancements in Federated Learning: Models, Methods, and Privacycited by 7
  7. 2023Quantifying and Defending against Privacy Threats on Federated Knowledge Graph Embeddingcited by 7
  8. 2025S2FGL: Spatial Spectral Federated Graph Learningcited by 7
  9. 2023Federated Knowledge Graph Completion via Latent Embedding Sharing and Tensor Factorizationcited by 5
  10. 2024Can Contrastive Learning Refine Embeddingscited by 5
  11. 2024Learning Federated Neural Graph Databases for Answering Complex Queries from Distributed Knowledge Graphscited by 4
  12. 2022FedRule: Federated Rule Recommendation System with Graph Neural Networkscited by 3
  13. 2024Personalized Federated Knowledge Graph Embedding with Client-Wise Relation Graphcited by 3
  14. 2024Communication-Efficient Federated Knowledge Graph Embedding with Entity-Wise Top-K Sparsificationcited by 3
  15. 2024Personalized One-shot Federated Graph Learning for Heterogeneous Clientscited by 3
  16. 2024Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligencecited by 2
  17. 2026Federated Personal Knowledge Graph Completion with Lightweight Large Language Models for Personalized Recommendationscited by 2
  18. 2026FedV-KGQA: Multi-Hop Question Answering over Vertically Partitioned Knowledge Graphscited by 2
  19. 2023FedSpectral+: Spectral Clustering using Federated Learningcited by 1
  20. 2023FedHGN: A Federated Framework for Heterogeneous Graph Neural Networkscited by 1
  21. 2023FedMKGC: Privacy-Preserving Federated Multilingual Knowledge Graph Completioncited by 1
  22. 2024Low-Dimensional Federated Knowledge Graph Embedding via Knowledge Distillationcited by 1
  23. 2025VGFL-SA: Vertical Graph Federated Learning Structure Attack Based on Contrastive Learningcited by 1
  24. 2025FedHERO: A Federated Learning Approach for Node Classification Task on Heterophilic Graphscited by 1
  25. 2025Hyper-modal Imputation Diffusion Embedding with Dual-Distillation for Federated Multimodal Knowledge Graph Completioncited by 1
  26. 2026Context Kubernetes: Declarative Orchestration of Enterprise Knowledge for Agentic AI Systemscited by 1
  27. 2022Secure Embedding Aggregation for Federated Representation Learningcited by 0
  28. 2022Application of Data Encryption in Chinese Named Entity Recognitioncited by 0
  29. 2024Query-Efficient Adversarial Attack Against Vertical Federated Graph Learningcited by 0
  30. 2025iN2V: Bringing Transductive Node Embeddings to Inductive Graphscited by 0
  31. 2026Inferring Sensitive Attributes from Knowledge Graph Embeddings: Attack and Defense Strategiescited by 0
  32. 2026FedV-KGQA in Practice: Design Lessons and an Interactive Prototypecited by 0
  33. 2026Cost Characterization of Vertically Partitioned Federated Knowledge Graphscited by 0
  34. 2026Federated Agent Optimizationcited by 0