Implementation of asynchronous federated learning in flower.

Federated Learning framework extending the nnUNet

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.

Federated Learning in Satellite Constellations using Flower

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.

FLEVEn — Federated Learning for Vehicular Environment

Federated Learning simulation using Flower with decentralized client training, secure aggregation concepts, and SHA-256 audit logging.

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

Privacy-preserving federated learning for NIH Chest X-ray classification. Demonstrates distributed AI that respects patient privacy by training models locally at hospitals and sharing only model updates.

Federated Learning with 1D-CNN for Web Attack Detection on Edge-IIoTset using the Flower Framework. This project explores both IID and Non-IID data partitions to evaluate federated performance in decentralized IoT environments.

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

FL practices for NLP

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

A Flower / PyTorch implementation for a participation-aware client selection strategy in Federated Learning