Flower framework for Federated Learning, with Fully Homomorphic Encryption integrated

List Flower resources

An AI Platform which utilize edge computing to provide sustainable AI solutions.

Basic federated learning pipeline using Flower and scikit‑learn on autism dataset.

Experiments of the FL in Healthcare project - MRI images use case - using Flower

Federated learning project with flower framework with CNN as classification mode

A repository to introduce different modern framework to detect arrythmia heart disease using federated learning frameworks .

Federated machine learning approach for heart disease prediction with privacy-preserving data collaboration.

Golang Flwr Client implementation

Federated learning system for edge devices using Flower (flwr). Implements FedAvg, non-IID Dirichlet data partitioning, hybrid data+model parallelism, split inference (83.7% bandwidth saving), adaptive 3-tier model serving, and per-round communication analysis. PyTorch + Python.

Adaptive Threshold-Based Federated Learning Framework using Flower to mitigate overgeneralization in non-IID data.

Federated Learning is simply the decentralized form of Machine Learning. Here we train a centralized Machine Learning model on decentralized data.

A thesis implementing and evaluating a framework for energy-aware federated learning, capable of both simulation and real-time monitoring.

This repository, consists of the packaged code from the experiments from https://github.com/aneesh-aparajit/fedGPT