federated-learning

by shaoxiongji

shaoxiongji / federated-learning

Federated Learning - PyTorch

288 Stars 98 Forks Last release: Not found MIT License 30 Commits 0 Releases

Available items

No Items, yet!

The developer of this repository has not created any items for sale yet. Need a bug fixed? Help with integration? A different license? Create a request here:

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 mainfed.py --dataset mnist --iid --numchannels 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.

Shaoxiong Ji, Shirui Pan, Guodong Long, Xue Li, Jing Jiang, and Zi Huang. Learning private neural language modeling with attentive aggregation. In the 2019 International Joint Conference on Neural Networks (IJCNN), 2019. [Paper] [Code]

Jing Jiang, Shaoxiong Ji, and Guodong Long. Decentralized knowledge acquisition for mobile internet applications. World Wide Web, 2020. [Paper]

We use cookies. If you continue to browse the site, you agree to the use of cookies. For more information on our use of cookies please see our Privacy Policy.