Historical Embedding-Guided Efficient Large-Scale Federated Graph Learning
Summary: FedAAS: scalable federated GCN training via historical embedding estimators + adaptive attention-based neighbor sampling, targeting large distributed graphs under privacy constraints. Key novelty is selective cross-client embedding sync to cut comm/compute while bounding staleness and preserving accuracy. (summarized by gpt-5.4-mini on May 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Anran Li (Nanyang Technological University)
- 2. Yuanyuan Chen (Nanyang Technological University)
- 3. Jian Zhang (Nanyang Technological University)
- 4. Mingfei Cheng (Singapore Management University)
- 5. Yihao Huang (Nanyang Technological University)
- 6. Yueming Wu (Nanyang Technological University)
- 7. Anh Tuan Luu (Nanyang Technological University)
- 8. Han Yu (Nanyang Technological University)
BibTeX Citation
@inproceedings{li_sigmod24,
title = {{Historical Embedding-Guided Efficient Large-Scale Federated Graph Learning}},
author = {Li, Anran and Chen, Yuanyuan and Zhang, Jian and Cheng, Mingfei and Huang, Yihao and Wu, Yueming and Luu, Anh Tuan and Yu, Han},
series = {{SIGMOD} '24},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3654947},
url = {https://dl.acm.org/doi/10.1145/3654947},
year = {2024}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,817 | OpenFGL: A Comprehensive Benchmark for Federated Graph Learning | 2025 | VLDB | 5.093636e-05 |
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Showing 0 of 0 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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