A server-edge fine-tuning federated learning framework claims up to 42% memory savings and 75% training time savings with 99.2% intrusion detection accuracy on NSL-KDD, though the evaluation design uses the test set for pre-training.
Fl- ids: Federated learning-based intrusion detection system using edge devices for transportation iot,
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Fine-Tuning Federated Learning-Based Intrusion Detection Systems for Transportation IoT
A server-edge fine-tuning federated learning framework claims up to 42% memory savings and 75% training time savings with 99.2% intrusion detection accuracy on NSL-KDD, though the evaluation design uses the test set for pre-training.