基于VGG19、efficientnet和Resnet50联邦学习的分心驾驶检测python源码+文档说明

# Federated Learning [](https://doi.org/10.5281/zenodo.4321561) This is partly the reproduction of the paper of [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://arxiv.org/abs/1602.05629) 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](main_nn.py) Federated learning with MLP and CNN is produced by: > python [main_fed.py](main_fed.py) See the arguments in [options.py](utils/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](https://github.com/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