TY - GEN
T1 - Is Averaging Always the Best? Improving Aggregation Method for Federated Knowledge Graph Embedding
AU - Liu, Yuanyi
AU - Zhang, Renyu
AU - Chen, Jia
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In recent years, along with the rapid development of big data and AI technologies, knowledge graphs have also experienced significant growth. The vectorized representation of entities and relations in knowledge graphs has proven beneficial for various knowledge graph-related applications. However, traditional knowledge graph embedding methods are designed for centralized graphs, which cannot effectively represent the distributed knowledge graphs in real-world while ensuring data security. In this paper, we improve the aggregation method for federated knowledge graph embedding and propose a Federated knowledge graph embedding model with CompGCN, which is called FedComp for short. FedComp is an innovative server-client framework which implement CompGCN for federated KGE, along with the design of three novel aggregation methods. We conduct link prediction experiments on two datasets to demonstrate the performance of our model.
AB - In recent years, along with the rapid development of big data and AI technologies, knowledge graphs have also experienced significant growth. The vectorized representation of entities and relations in knowledge graphs has proven beneficial for various knowledge graph-related applications. However, traditional knowledge graph embedding methods are designed for centralized graphs, which cannot effectively represent the distributed knowledge graphs in real-world while ensuring data security. In this paper, we improve the aggregation method for federated knowledge graph embedding and propose a Federated knowledge graph embedding model with CompGCN, which is called FedComp for short. FedComp is an innovative server-client framework which implement CompGCN for federated KGE, along with the design of three novel aggregation methods. We conduct link prediction experiments on two datasets to demonstrate the performance of our model.
KW - Aggregation Algorithm
KW - CompGCN
KW - FedE
KW - Federated Learning
KW - Knowledge Graph Embedding (KGE)
UR - https://www.scopus.com/pages/publications/85179624682
U2 - 10.1109/ICNSC58704.2023.10318978
DO - 10.1109/ICNSC58704.2023.10318978
M3 - 会议稿件
AN - SCOPUS:85179624682
T3 - ICNSC 2023 - 20th IEEE International Conference on Networking, Sensing and Control
BT - ICNSC 2023 - 20th IEEE International Conference on Networking, Sensing and Control
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 20th IEEE International Conference on Networking, Sensing and Control, ICNSC 2023
Y2 - 25 October 2023 through 27 October 2023
ER -