arXiv (All)
open access
HybridFLow: SDN-Orchestrated Client Partitioning for Hybrid Federated Learning
Cross-silo Federated Learning (FL) enables geographically distributed institutions to collaboratively train machine learning models without sharing raw data. In wide-area deployments, however, communication delays often dominate round completion time and exacerbate the straggler effect. Hybrid FL ad
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
- Adaptive Client Clustering and Coordination for Federated Learning Workflow Management in Edge Networks#1118256
- Adaptive Client Clustering and Coordination for Federated Learning Workflow Management in Edge Networks#1108726
- Scheduling algorithms for the optimization of distributed machine learning models on heterogeneous resources#1146095
- Adaptive Bayesian Partner Selection for Federated Clinical Centers#1110803
- Adaptive Bayesian Partner Selection for Federated Clinical Centers#919337
Record · ID 997289
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