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federated-learning

A PyTorch Implementation of Federated Learning

Model DevelopmentTraining toolkitsPython
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创建于 2018-03-30 · 更新于 2026-10-12 · 今日第 10115 名
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关键参数与规格
关键参数与规格
参数值
语言Python
类别Model Development
子类别Training toolkits
关键词Federated learning
星标数1,515
分叉数393
贡献者3
创建日期2018-03-30
更新日期2026-10-12
1 天星标数0
7 天星标数0
排名10,115
上次排名10,129
排名变化14
7 天星标排名12,512
上次 7 天星标排名12,555
7 天星标排名变化43
总星标排名8,030
上次总星标排名8,019
总星标排名变化-11
README 摘录

This is partly the reproduction of the paper of Communication-Efficient Learning of Deep Networks from Decentralized Data Only experiments on MNIST and CIFAR10 (both IID and non-IID) is produced by far. Note: The scripts will be slow without the implementation of parallel computing. 4 Run The MLP and CNN models are produced by: python main_nn. py Federated learning with MLP and CNN is produced by: python main_fed. Results MNIST Results are shown in Table 1 and

Table 2, with the parameters C=0. results of 10 epochs training with the learning rate of 0. results of 50 epochs training with the learning rate of 0. 81% | Ackonwledgements Acknowledgements give to youkaichao. References McMahan, Brendan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. Communication-Efficient Learning of Deep Networks from Decentralized Data. In Artificial Intelligence and Statistics (AISTATS), 2017. A PyTorch Implementation of Federated Learning.

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shaoxiongji/federated-learning 有 1,515 个星标和 393 个分叉。

仓库排名是多少？

shaoxiongji/federated-learning 排名第 10,115。

记录的语言和类别是什么？

shaoxiongji/federated-learning 有 1,515 个星标，语言为 Python，类别为 Model Development。

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shaoxiongji/federated-learning 与 IBM/pytorch-seq2seq 比较，后者排名第 10,114，有 1,515 个星标。

README

Federated Learning

This is partly the reproduction of the paper of Communication-Efficient Learning of Deep Networks from Decentralized Data
Only experiments on MNIST and CIFAR10 (both IID and non-IID) is produced by far.

Note: The scripts will be slow without the implementation of parallel computing.

Requirements

python>=3.6
pytorch>=0.4

Run

The MLP and CNN models are produced by:

python main_nn.py

Federated learning with MLP and CNN is produced by:

python main_fed.py

See the arguments in options.py.

For example:

python main_fed.py --dataset mnist --iid --num_channels 1 --model cnn --epochs 50 --gpu 0

--all_clients for averaging over all client models

NB: for CIFAR-10, num_channels must be 3.

Results

MNIST

Results are shown in Table 1 and Table 2, with the parameters C=0.1, B=10, E=5.

Table 1. results of 10 epochs training with the learning rate of 0.01

Model Acc. of IID Acc. of Non-IID
FedAVG-MLP 94.57% 70.44%
FedAVG-CNN 96.59% 77.72%

Table 2. results of 50 epochs training with the learning rate of 0.01

Model Acc. of IID Acc. of Non-IID
FedAVG-MLP 97.21% 93.03%
FedAVG-CNN 98.60% 93.81%

Ackonwledgements

Acknowledgements give to youkaichao.

References

McMahan, Brendan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. Communication-Efficient Learning of Deep Networks from Decentralized Data. In Artificial Intelligence and Statistics (AISTATS), 2017.

Cite As

Shaoxiong Ji. (2018, March 30). A PyTorch Implementation of Federated Learning. Zenodo. http://doi.org/10.5281/zenodo.4321561

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