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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 [azizi.mohdariffin@gmail.com]