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.

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

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

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.