# HFKG-RFE: An algorithm for heterogeneous federated knowledge graph

## TL;DR

This paper introduces HFKG-RFE, a new algorithm that combines federated learning and knowledge graphs to handle data heterogeneity and improve model performance.

## Contribution

The novel HFKG-RFE algorithm addresses data heterogeneity in federated knowledge graphs using comparative learning and a new server aggregation method.

## Key findings

- HFKG-RFE shows stable improvement in experiments with datasets like DDB14, WN18RR, and NELL.
- The proposed RFE model and server aggregation algorithm enhance the performance of federated knowledge graph training.
- The algorithm effectively limits model drift and handles data heterogeneity across clients.

## Abstract

Federated learning ensures that data can be trained globally across clients without leaving the local environment, making it suitable for fields involving privacy data such as healthcare and finance. The knowledge graph technology provides a way to express the knowledge of the Internet into a form more similar to the human cognitive world. The training of the knowledge graph embedding model is similar to that of many models, which requires a large amount of data for learning to achieve the purpose of model development. The security of data has always been a focus of public attention, and driven by this situation, knowledge graphs have begun to be combined with federated learning. However, the combination of the two often faces the problem of federated data statistical heterogeneity, which can affect the performance of the training model. Therefore, An Algorithm for Heterogeneous Federated Knowledge Graph (HFKG) is proposed to solve this problem by limiting model drift through comparative learning. In addition, during the training process, it was found that both the server aggregation algorithm and the client knowledge graph embedding model performance can affect the overall performance of the algorithm.Therefore, a new server aggregation algorithm and knowledge graph embedding model RFE are proposed. This paper uses the DDB14, WN18RR, and NELL datasets and two methods of dataset partitioning to construct data heterogeneity scenarios for extensive experiments. The experimental results show a stable improvement, proving the effectiveness of the federated knowledge graph embedding aggregation algorithm HFKG-RFE, the knowledge graph embedding model RFE and the federated knowledge graph relationship embedding aggregation algorithm HFKG-RFE formed by the combination of the two.