# EnDuSecFed: an ensemble approach for privacy preserving Federated Learning with dual-security framework for sustainable healthcare

## TL;DR

This paper introduces a secure Federated Learning framework for healthcare that improves privacy and model reliability using encryption and intrusion detection.

## Contribution

A dual-security framework combining symmetric encryption and intrusion detection for privacy-preserving Federated Learning in healthcare.

## Key findings

- The proposed system enhances privacy and robustness compared to traditional Federated Learning.
- The ensemble method achieved 99% accuracy among tested models like Logistic Regression and Random Forest.

## Abstract

Recent advances in Artificial Intelligence have highlighted the role of Machine Learning in healthcare decision-making, but centralized data collection raises significant privacy risks. Federated Learning addresses this by enabling collaborative training across multiple clients without sharing raw data. However, Federated Learning remains vulnerable to security threats that can compromise model reliability. This paper proposes a dual-security Federated Learning framework that integrates Fernet Symmetric Encryption for secure transmission of model updates using symmetric encryption and an Intrusion Detection System to detect anomalous client behavior. Experiments on a publicly available healthcare dataset show that the proposed system enhances privacy and robustness compared to traditional FL. Among tested models, including Logistic Regression, Random Forest, and SVC, the ensemble method achieved the best performance with 99% accuracy.