This is a simple demonstration of how MLflow can be utilised to track and trace local ML models trained in a Federated Learning set-up.
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Updated
Jun 7, 2024 - Python
This is a simple demonstration of how MLflow can be utilised to track and trace local ML models trained in a Federated Learning set-up.
Simple and customizable framework that can serve as a boilerplate for your Federated simulations.
A research-oriented federal learning framework.
Federated Learning (FL) experiment simulation in Python.
(CVPR 2024) Official Implementation of "FedSOL: Stabilized Orthogonal Learning with Proximal Restrictions in Federated Learning"
This repo is about federated learning implementation using FLOWER framework for beginners.
A Federated Deep Learning Simulator Framework (FedSim). This simulator was developed under supervision of Dr. Keyvan RahimiZadeh.
FedRay: a Research Framework for Federated Learning based on Ray
A decentralized federated learning framework enabling secure P2P model training on edge devices.
NEBULA: A Platform for Decentralized Federated Learning
🌐 Lightweight Federated Learning Framework 🚀 | Simplifying federated learning experiments with a focus on algorithms not infrastructure | 🧪 Easy setup and run
A Docker-Based Federated Learning Framework Design and Deployment for Multi-modal Data Stream Classification
A framework for fast federated learning algorithm verification based on Tensorflow (Only suitable for academic research)
Flexe - The open source federated learning for vehicular network simulation framework.
SemiPFL: Personalized Semi-Supervised Federated Learning Framework for Edge Intelligence
Centralized Federated Learning using WebSockets and TensorFlow
[ICLR2023] Towards Understanding and Mitigating Dimensional Collapse in Heterogeneous Federated Learning (https://arxiv.org/abs/2210.00226)
FedKeeper is a client-based python tool for propagating FL-client functions over FaaS fabric. Its main objective is to act as a manager or keeper of various client functions distributed over different FaaS platforms.
Federated learning with homomorphic encryption enables multiple parties to securely co-train artificial intelligence models in pathology and radiology, reaching state-of-the-art performance with privacy guarantees.
A flexible, modular, and easy to use library to facilitate federated learning research and development in healthcare settings
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