Abdulrahman K. Alnaim

Federated Continual Learning for Encrypted Traffic Classification at the Network Edge Under Asynchronous Concept Drift

Concept drift can degrade encrypted-traffic classifiers deployed at the network edge as applications, protocols, and usage patterns evolve. This paper formulates federated continual learning under asynchronous real- and virtual drift and proposes DriftGuard, a framework combining a two-level per-node detector, selectiv...