Cost Characterization of Vertically Partitioned Federated Knowledge Graphs (arXiv)
This arXiv cs.AI paper formalizes vertical partitioning of federated knowledge graphs and compares four partitioning strategies (semantic domain grouping, frequency-balanced, co-occurrence graph-cut, and random) across five metrics: communication cost, candidate index size, cross-silo path length, load balance, and query latency. Using a fixed federated QA architecture (TransE embeddings and a frozen BERT encoder) on MetaQA and PathQuestion benchmarks and three silo configurations, the authors find that three of the five metrics are determined by the graph structure and silo count, reducing the design trade-off primarily to cross-silo path length versus load balance, and that the locality-versus-balance trade-off weakens as silo count increases.