NTC-FL-Edge-XAI

Paper: Leveraging Federated Learning and XAI for Private and Lightweight Edge Training In Network Traffic Classification

Deployed on:

FL Server

• HP Pavilion 14
• Ryzen 5 (8 Core CPU)
• 16GB RAM
• 100 GB SSD Storage

FL Client

• Nvidia Jetson Nano
• Quad-core ARM A57 CPU
• 128-core Maxwell GPU
• 4 GB RAM
• 64 GB eMMC Storage

Software

• Tensorflow v2.6.0
• Flower 1.14.0
• Keras v2.11.0
• DeepSHAP v0.41.0

Deep Learning Technique

1. MLP
2. 1D-CNN

To deploy:

1. Download dataset here: https://www.unb.ca/cic/datasets/vpn.html and put in folder and run ISCX-VPN2016-pre-processing-v2.ipynb & ISCX-VPN2016-pre-processing_combine.ipynb script from Preprocessing folder
2. Put the processed raw data into /content/DATA and run preprocessraw.py script from Preprocessing folder
3. run the script from Centralized_FL_Experiment folder to run first experiment train the initial model
4. run the script from XAI_Experiment folder to perform feature selection and run the second experiment
5. run the evaluate.py script to evaluate model performance

For any inquiries you can email [[email protected]]