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HybridFLow: SDN-Orchestrated Client Partitioning for Hybrid Federated Learning
HybridFLow uses SDN topology visibility to partition federated-learning clients into sync/async groups, reaching 80% accuracy 33-40% faster than SmartFLow.
HybridFLow is a closed-loop, SDN-driven orchestration framework for hybrid federated learning that integrates network-layer intelligence into cross-silo training. It leverages the SDN controller's global topology view to generate calibrated per-client communication-time estimates, partitioning clients into synchronous and asynchronous groups while balancing round latency and update staleness, with measured times fed back after each round. Across multiple network topologies it reaches 80% target accuracy 33-40% faster than SmartFLow and cuts average round duration by 30-40 seconds, while FedAsync fails to reach target accuracy under non-IID data.
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Lightweight Zero Trust via Automotive SDN
Researchers map automotive SDN with MACsec/MKA and CORECONF/YANG to NIST SP 800-207, satisfying five of seven Zero Trust tenets without added infrastructure.
Zonal in-vehicle networks ship Ethernet, MACsec, and TSN but treat the network itself as trusted, with no standardized runtime way to revoke access, rotate keys, or contain a compromised ECU. The paper first analyzes what Open Alliance TC17 v1.0 MACsec/MKA with pre-shared CAKs already provides against the seven NIST SP 800-207 Zero Trust tenets. It then adds CORECONF/YANG management per Open Alliance TC19, mapping the SDN Controller and Agents one-to-one onto NIST's PE, PA, and PEP, and instantiates a YANG-based network-access-control flow and key-management scheme. The result fully covers five of the seven tenets and partially covers two, without any ZTA-specific infrastructure.
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