FedAWAC

CIFAR-10 and CIFAR-100 datasets comprise images of $10$ and $100$ categories, respectively, with a fixed size of 32x32 pixels in color. We train a Convolutional Neural Network (CNN) model to classify images, which consists of $2$ convolutional layers (5x5, each activated by ReLU and followed by 2\x2 max pooling), 2 fully connected layers, and Softmax normalizes the final output.