# FedE: Embedding Knowledge Graphs in Federated Setting

arXiv: https://arxiv.org/abs/2010.12882 · HTML: https://mlsanity.com/paper/2010.12882/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2020.

## Built on by (most cited first, 30 of 46)

- [Federated Graph Classification over Non-IID Graphs](https://mlsanity.com/paper/2106.13423/) (2021)
- [Federated Learning on Non-IID Graphs via Structural Knowledge Sharing](https://mlsanity.com/paper/2211.13009/) (2022)
- [From Distributed Machine Learning to Federated Learning: A Survey](https://mlsanity.com/paper/2104.14362/) (2021)
- [Federated Graph Neural Networks: Overview, Techniques and Challenges](https://mlsanity.com/paper/2202.07256/) (2022)
- [Differentially Private Federated Knowledge Graphs Embedding](https://mlsanity.com/paper/2105.07615/) (2021)
- [Heterogeneous Federated Knowledge Graph Embedding Learning and Unlearning](https://mlsanity.com/paper/2302.02069/) (2023)
- [Federated Graph Learning -- A Position Paper](https://mlsanity.com/paper/2105.11099/) (2021)
- [Meta-Knowledge Transfer for Inductive Knowledge Graph Embedding](https://mlsanity.com/paper/2110.14170/) (2021)
- [A Vertical Federated Learning Framework for Graph Convolutional Network](https://mlsanity.com/paper/2106.11593/) (2021)
- [Federated Learning of Molecular Properties with Graph Neural Networks in a Heterogeneous Setting](https://mlsanity.com/paper/2109.07258/) (2021)
- [Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregation](https://mlsanity.com/paper/2203.09553/) (2022)
- [Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the Federated Setting](https://mlsanity.com/paper/2205.04692/) (2022)
- [Federated Graph Machine Learning: A Survey of Concepts, Techniques, and Applications](https://mlsanity.com/paper/2207.11812/) (2022)
- [Deep Efficient Private Neighbor Generation for Subgraph Federated Learning](https://mlsanity.com/paper/2401.04336/) (2024)
- [Federated Graph Learning with Structure Proxy Alignment](https://mlsanity.com/paper/2408.09393/) (2024)
- [Safety in Graph Machine Learning: Threats and Safeguards](https://mlsanity.com/paper/2405.11034/) (2024)
- [Entity-Agnostic Representation Learning for Parameter-Efficient Knowledge Graph Embedding](https://mlsanity.com/paper/2302.01849/) (2023)
- [Advancements in Federated Learning: Models, Methods, and Privacy](https://mlsanity.com/paper/2302.11466/) (2023)
- [Quantifying and Defending against Privacy Threats on Federated Knowledge Graph Embedding](https://mlsanity.com/paper/2304.02932/) (2023)
- [S2FGL: Spatial Spectral Federated Graph Learning](https://mlsanity.com/paper/2507.02409/) (2025)
- [Federated Knowledge Graph Completion via Latent Embedding Sharing and Tensor Factorization](https://mlsanity.com/paper/2311.10341/) (2023)
- [Can Contrastive Learning Refine Embeddings](https://mlsanity.com/paper/2404.08701/) (2024)
- [Learning Federated Neural Graph Databases for Answering Complex Queries from Distributed Knowledge Graphs](https://mlsanity.com/paper/2402.14609/) (2024)
- [FedRule: Federated Rule Recommendation System with Graph Neural Networks](https://mlsanity.com/paper/2211.06812/) (2022)
- [Personalized Federated Knowledge Graph Embedding with Client-Wise Relation Graph](https://mlsanity.com/paper/2406.11943/) (2024)
- [Communication-Efficient Federated Knowledge Graph Embedding with Entity-Wise Top-K Sparsification](https://mlsanity.com/paper/2406.13225/) (2024)
- [Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligence](https://mlsanity.com/paper/2407.15320/) (2024)
- [Personalized One-shot Federated Graph Learning for Heterogeneous Clients](https://mlsanity.com/paper/2411.11304/) (2024)
- [Federated Personal Knowledge Graph Completion with Lightweight Large Language Models for Personalized Recommendations](https://mlsanity.com/paper/2603.13264/) (2026)
- [FedV-KGQA: Multi-Hop Question Answering over Vertically Partitioned Knowledge Graphs](https://mlsanity.com/paper/2608.24846/) (2026)

## 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.