arXiv (All)
open access
More Is Better? Exploring Latent Correspondence in Federated Hypergraph Learning
Federated hypergraph learning with latent cross-client hyperedge correspondence poses fundamental identifiability and computational challenges, as the same local release can correspond to multiple global hypergraph structures. We characterize the tractability of recovering these latent correspondenc
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- More Is Better? Exploring Latent Correspondence in Federated Hypergraph Learning#1482965
- Batch Before You Lift: Scalable Topological Deep Learning on Large Graphs#1249671
- Batch Before You Lift: Scalable Topological Deep Learning on Large Graphs#1242759
- From Coordinate Matching to Structural Alignment: Rethinking Prototype Alignment in Heterogeneous Federated Learning#168271
- Rethinking One-Shot Federated Graph Learning: Training-Free Statistical Estimation#973757
Record · ID 1490757
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive