Research Article
Local Epochs Inefficiency Caused by Device Heterogeneity in Federated Learning
Table 1
The description of layer features.
| Kind of features | Features | Description |
| Common features | Activation function | The activation function of neuron output, the common ones are sigmoid, tanh, and relu, etc. | Optimizer | The optimization method of the model, the common ones are SGD, Adadelta, Adagrad, momentum, Adam, and RMS prop, etc. | Dense features | Number of inputs | Since the MLP layers are fully connected, the input of each layer comes from the output of the previous layer. | Number of neurons | The number of neurons. | Convolutional features | Matrix size | The size of the input data. | Kernel size | The size of convolutional kernel. | Input depth | The number of input channels. | Output depth | The number of output channels. | Stride size | The convolution step size of convolution kernel. | Input padding | The number of edge padding after convolution. | Hardware features | GPU clock speed | GPU clock cycle speed. | GPU memory bandwidth | GPU bandwidth. | GPU core count | The number of GPU processing units, which represents the number of CUDA cores in NVIDIA GPU. |
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