GitHub - vatsalgupta13/Privacy-Preserving-Federated-Learning-Model-For-Email-Spam-Detection: This project aims to employ the federated learning technique to classify emails as spam or ham (not spam) while ensuring user privacy and data confidentiality. The model manifested in our project can help people recognize the nature of mails received without sharing their private information.

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This project aims to employ the federated learning technique to classify emails as spam or ham (not spam) while ensuring user privacy and data confidentiality. The model manifested in our project can help people recognize the nature of mails received without sharing their private information.

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Name		Name	Last commit message	Last commit date
Latest commit History 1 Commit
Data		Data
Final Report		Final Report
Email Spam Detection - GRU (with FL).py		Email Spam Detection - GRU (with FL).py
Email Spam Detection - Logistic Regression (with Homomorphic Encryption FL).py		Email Spam Detection - Logistic Regression (with Homomorphic Encryption FL).py
Email Spam Detection - Neural Networks (without FL).py		Email Spam Detection - Neural Networks (without FL).py
Homomorphic Encryption - Basic Working.py		Homomorphic Encryption - Basic Working.py
Multiple Models.py		Multiple Models.py
Preprocessing.py		Preprocessing.py
Selfmade_GRU.py		Selfmade_GRU.py
readme.txt		readme.txt
test_data.csv		test_data.csv
topWords1.png		topWords1.png
train_data.csv		train_data.csv

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How to run the codes?

Kindly download and install all the required packages (mentioned below) before running the code:
1. numpy
2. matplotlib
3. sklearn
4. pandas
5. string
6. nltk
7. sacremoses
8. torch
9. re
10. phe
11. keras
12. os
13. glob
14. syft
15. pickle

Kindly keep the folder structure same to run the codes since dataset path has been mentioned in the code. For "Email Spam Detection - GRU (with FL).py" - kindly run "Preprocessing.py" before running the code.

*** Other details about the project available in the project report. Be sure to check that out ***