Flower framework for Federated Learning, with Fully Homomorphic Encryption integrated
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Updated
Jun 3, 2024 - Python
Flower framework for Federated Learning, with Fully Homomorphic Encryption integrated
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 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.
Federated learning project with flower framework with CNN as classification mode
Federated machine learning approach for heart disease prediction with privacy-preserving data collaboration.
Golang Flwr Client implementation
Federated Learning based Intrusion Detection and Prevention System for VANET environments using FedAvg and FedProx.
Adaptive Threshold-Based Federated Learning Framework using Flower to mitigate overgeneralization in non-IID 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
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