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)

Paper ID: 6909
Venue: SIGMOD
Year: 2024
Pagerank: 4.456315e-05
Overall Rank: 8,740 | 39.20%
DOI: 10.1145/3654947

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Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
10,545	OpenFGL: A Comprehensive Benchmark for Federated Graph Learning	2025	VLDB	4.1945683e-05

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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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