Summary of Low-dimensional Federated Knowledge Graph Embedding Via Knowledge Distillation, by Xiaoxiong Zhang et al.
Low-Dimensional Federated Knowledge Graph Embedding via Knowledge Distillation
by Xiaoxiong Zhang, Zhiwei Zeng, Xin Zhou, Zhiqi Shen
First submitted to arxiv on: 11 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Federated Knowledge Graph Embedding (FKGE) method enables collaborative learning of entity and relation embeddings from distributed Knowledge Graphs (KGs), while preserving data privacy. The approach involves multiple client-server communication rounds, where communication efficiency is crucial. To address the challenges of high-dimensional embeddings, a light-weight component based on Knowledge Distillation (KD) is proposed, tailored specifically for FKGE methods. This component, titled FedKD, facilitates the low-dimensional student model to mimic the score distribution of triples from the high-dimensional teacher model using KL divergence loss. The effectiveness of FedKD is supported by extensive experiments on three datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FKGE helps computers learn from many different sources of data without sharing that data with each other. This keeps the data private, which is important for security. To make this work, FKGE has to share information between computers in a way that’s efficient and fast. One problem is that the data gets too big and takes up too much space or makes computations slow. Researchers came up with a new way to compress the data using something called Knowledge Distillation (KD). This helps smaller models learn from bigger ones without needing all the extra information. The new method, called FedKD, helps computers learn better and faster while keeping their data private. |
Keywords
» Artificial intelligence » Embedding » Knowledge distillation » Knowledge graph » Student model » Teacher model
