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.
《DCFL: Non-IID Awareness Dataset Condensation Aided Federated Learning》的部分代码 因为整体内容太大了，所以有选择的挑选上传 github，内容有些乱，也需要自己去在整理与看，希望能有一些帮助
Las características principales de jlussh/dcfl son: Federated Learning.
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