Hybrid Distributed Learning With Knowledge Distillation for Resource-Efficient Intrusion Detection in Distributed Networks
A novel AI-driven distributed NIDS that considers the computing capabilities of resource-constrained nodes while enabling efficient learning in distributed environments is proposed and can achieve accuracy comparable to a centralized model while reducing local computational overhead and maintaining stable convergence u...
Cheolhee Park, Kyungmin Park, Jihyeon Song et al.
· IEEE Internet of Things Jour... · 0 citations