Implementation of asynchronous federated learning in flower.

This project introduces a system that utilizes the Flower framework, along with the ESP32 microcontroller and the TinyDB database, for stress classification. The system collects and processes real-time biomarker data, enabling local model training on edge devices.

A federated learning-based intrusion detection system designed for securing edge IoT networks through decentralized anomaly detection and privacy-preserving intelligence sharing.

This repository provides a comprehensive solution and codebase for the migration from centralized to federated learning. It demonstrates centralized training, its drawbacks, and how federated learning addresses these issues. It also serves as a tutorial to guide users through the transition process.

This API caters to data scientists, simplifying remote host communication with service endpoints. It allows users to efficiently manage flower federated learning clusters.

federated learning framework built with Flower and PyTorch to evaluate the robustness of FL systems under data poisoning attacks.

This repository contains the code for the the research project submitted to be able to graduate in masters of computing.

This repository features a federated learning system designed for intrusion detection in IoT networks, ensuring data privacy while maintaining high accuracy. The project utilizes the Flower framework and includes essential components like data processing, server-client architecture, and SSL certificates for secure communication. 🐙🌐

FL practices for NLP