Coding a Multimodal (Vision) Language Model from scratch in PyTorch with full explanation: https://www.youtube.com/watch?v=vAmKB7iPkWw

Handy PyTorch implementation of Federated Learning (for your painless research)

Heterogeneous Federated Learning: State-of-the-art and Research Challenges

PyTorch implementation of Straggler-Aware Layer-Wise Low-Latency Federated Learning (IEEE TCOM 2024)

This is scheduler extender (THPA)

This contains the custom kube-controller-manager of THPA

Performance Evaluvation Of Federated Learning in Edge Computing Environment

Kube Edge Repository for Local Scheduling in Kube Edge based Edge Computing Environment

Demo to show how Federated Learning Works with Flower.

Tutorial on using flower Simulation in a multi-node environment

A flexible Federated Learning Framework based on PyTorch, simplifying your Federated Learning research.

Simple customizable framework based on Flower for Federated Learning

FLDQN: Cooperative Multi-Agent Federated Reinforcement Learning for Solving Travel Time Minimization Problems in Dynamic Environments Using SUMO Simulation

[ICLR 2021] HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients

📦 Collect some Asynchronous Federated Learning papers.

[ICLR 2024] FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices Using a Computing Power-Aware Scheduler

Federated Learning Simulator (FLSim) is a flexible, standalone core library that simulates FL settings with a minimal, easy-to-use API. FLSim is domain-agnostic and accommodates many use cases such…

A Langchain app that allows you to chat with multiple PDFs

Semi-Asynchronous Federated Averaging (SAFA)

Experiments of the DAI in Healthcare project - skin lesions images use case - using Flower

FedEasy is an intuitive powerful yet simple to use Federated Learning framework. Our goal is to streamline the process of setting up and running federated learning experiments with ease, making adv…

Material workbench for the master-level course CS-E4740 "Federated Learning"

PyTorch implementation of: D. Shenaj, M. Toldo, A. Rigon and P. Zanuttigh, “Asynchronous Federated Continual Learning”, CVPR 2023 Workshop on Federated Learning for Computer Vision (FedVision).

A demo of a new federated learning Python framework called flower for purposes of a live demo.

An open-source framework for machine learning and other computations on decentralized data.