This repository contains the source code for the paper DYNAFED: Tackling Client Data Heterogeneity with Global Dynamics. Our paper is accepted by CVPR2023 and is available on arXiv: link.
Las características principales de pipilurj/dynafed son: Federated Learning.
Las alternativas de código abierto para pipilurj/dynafed incluyen: adap/flower — Flower is a federated learning framework and distributed machine learning orchestrator designed to train models across… ailabstw/harmonia — Federated Learning Made Easy. anonymifish/fed-distribution-matching — @InProceedings{Xiong2023CVPR, author = {Xiong, Yuanhao and Wang, Ruochen and Cheng, Minhao and Yu, Felix and Hsieh,… easyfl-ai/easyfl — An easy-to-use federated learning platform. feddg23/feddg-main — To setup an environment, please run. a514514772/fedlap-dp — Note: this repo is implemented in a sequentially running manner. We are working on a parallel implementation with the…
Flower is a federated learning framework and distributed machine learning orchestrator designed to train models across decentralized devices. It functions as a privacy-preserving toolkit that enables model training and data analysis on local hardware, ensuring raw data remains on the device while contributing to a synchronized global model. The system employs an agnostic wrapper and integrator to connect diverse machine learning libraries, allowing different frameworks to operate within the same training loop. It uses a remote procedure call orchestrator to manage the exchange of model weight
@InProceedings{Xiong2023CVPR, author = {Xiong, Yuanhao and Wang, Ruochen and Cheng, Minhao and Yu, Felix and Hsieh, Cho-Jui}, title = {FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision…
Note: this repo is implemented in a sequentially running manner. We are working on a parallel implementation with the Flower framework.
