# A Review and Experimental Evaluation on Split Learning
## 3 Experimental Evaluation
### Experimental Setup  
Among all experiments, we trained each model using SGD with a fixed learning rate of 0.01 on mini-batches of size 64. We evaluated different SL paradigms on five commonly used datasets, namely MNIST, CIFAR-10, CIFAR-100, Adult, and ogbg-molhiv, which cover a wide range of data types, including images, tabular data, and graphs. The detailed information for each dataset is introduced as follows. * MNIST [45] contains handwritten digits sampled from postal codes (available online: [[http://yann.lecun.com/exdb/mnist/](http://yann.lecun.com/exdb/mnist/)]([http://yann.lecun.com/exdb/mnist/](http://yann.lecun.com/exdb/mnist/)) (accessed on 10 February 2025)). It comprises a total of 70,000 samples and is divided into 60,000 training samples and 10,000 testing samples. The digits are placed in the center of a \(28\times 28\) grid. The goal of the multi-class classification task is to recognize the handwritten digits from 0 to 9. On this dataset, we use LeNet-5 [45] for training and split the network layers in the first block (after the 2D MaxPool layer) so that the client-side model consists of a convolutional layer, a ReLU activation layer, and a 2D MaxPool layer. * CIFAR [61] has two different variants, CIFAR-10 and CIFAR-100, which contain 60,000 32 \(\times\) 32 RGB color images with 10 and 100 class labels, respectively. Images are uniformly distributed over classes, and each image has only one label. The dataset is divided into 50,000 training samples and 10,000 testing samples. The task is to classify objects in the images. Compared to MNIST, CIFAR contains real-world objects, introducing more noise and fewer similar features between classes, making it more difficult for classification on non-IID data. In particular, different SL paradigms may learn at different rates and be more sensitive to non-IID data. ResNet56 [109] is employed on the CIFAR-10 dataset and ResNet50 is utilized on the CIFAR-100 dataset. The networks are split at the first ReLU activation layer of both ResNet models, which means that the client-side model consists of a convolutional layer, a batch normalization layer, and a ReLU activation layer. * Ogbg-molhiv is obtained from the Open Graph Benchmark repository (available online: [[https://ogb.stanford.edu/docs/graphprop/](https://ogb.stanford.edu/docs/graphprop/)]([https://ogb.stanford.edu/docs/graphprop/](https://ogb.stanford.edu/docs/graphprop/)) (accessed on 10 February 2025)). It consists of molecular samples and labels indicating whether the molecules can inhibit HIV replication. The goal of this dataset is to learn a function mapping from the molecular to the label. This task can be modeled as a graph learning task wherein a molecule can be naturally treated as a graph. Specifically, for a given molecule, atoms are represented as nodes, and chemical bonds between them are represented as edges. The node feature is a nine-dimensional vector that contains atomic number, chirality, and other atomic characteristics. The edge feature is three-dimensional. The task in our experiments is a typical case of molecular property prediction, which plays a key role in the AI-drug domain. In real-world scenarios, the drug data are always distributed over different pharmaceutical companies and cannot be shared due to business concerns. Therefore, how to build an ML model without sharing the raw drug data is a hot topic in the research community. To the best of our knowledge, we are the first to apply SL paradigms to this problem and have preliminarily verified its effectiveness. On the dataset, we use a Graph Convolutional Network (GCN) model split at the first embedding layer. * Adult is a classic dataset with 48,842 instances (available online: [[https://archive.ics.uci.edu/dataset/20/census+income](https://archive.ics.uci.edu/dataset/20/census+income)]([https://archive.ics.uci.edu/dataset/20/census+income](https://archive.ics.uci.edu/dataset/20/census+income)) (accessed on 10 February 2025)) downloaded from the UCI ML repository.