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 addresses this challenge by combining synchronous and asynchronous client participation, but effective partitioning requires visibility into network conditions such as shared bottlenecks, link utilization, and path contention that individual clients cannot observe. We present HybridFLow, a closed-loop
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- PossiblePossibly related (embedding) · 53%Optimized IoT clustering and assignment in semi-synchronous federated learning - Nature →
- FuzzySimilar title/name (fuzzy) · 84%amitness/learning →
“Fuzzy title match (0.92): “HybridFLow: SDN-Orchestrated Client Partitioning for Hybrid ” ≈ “amitness/learning””
- LinkedLinked via arxiv author · 85%Osama Abu Hamdan →
“HybridFLow: SDN-Orchestrated Client Partitioning for Hybrid Federated Learning”
- LinkedLinked via arxiv author · 85%Rabin Pandey →
“HybridFLow: SDN-Orchestrated Client Partitioning for Hybrid Federated Learning”
- LinkedLinked via arxiv author · 85%Zihao Cheng →
“HybridFLow: SDN-Orchestrated Client Partitioning for Hybrid Federated Learning”
- LinkedLinked via arxiv author · 85%Engin Arslan →
“HybridFLow: SDN-Orchestrated Client Partitioning for Hybrid Federated Learning”
- LinkedLinked via arxiv author · 85%Md Arifuzzaman →
“HybridFLow: SDN-Orchestrated Client Partitioning for Hybrid Federated Learning”
- FuzzyOverlapping authors or contributors · 62%DietrichGebert/ponytail →
“Shared author/contributor keys: cheng”