# A Review and Experimental Evaluation on Split Learning
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Domain** & **Topic** & **References** & **Summary of Contributions** \\ \hline \multirow{10}{*}{FL} & Evaluation & [22] & Compare the performance of different FL methods. \\  & Concept and Method & [23; 24; 25] & Provide an overview of basic FL types and methods. \\  & Application & [26] & Review the application of FL in the industrial engineering domain. \\  & Privacy and Security & [27; 28] & Discuss the security and privacy issues in FL. \\  & Framework & [29; 30; 31; 32; 33] & Provide a general framework and benchmark for FL. \\  & Edge Computing & [34] & Discuss the design of FL methods in the Internet of Things. \\  & Method and Application & [35] & Outline FL types, methods, and perspective applications. \\  & Application & [36] & Discuss the applications and future prospects of FL in the healthcare domain. \\  & Mechanism Design & [37] & Review the incentive mechanisms designed for FL. \\  & Mechanism Design & [38] & Review the model aggregation mechanisms for FL. \\  & Application & [39; 40] & Discuss the application of FL in intrusion detection. \\  & Edge Computing & [41] & Review lightweight FL methods on resource-constrained devices. \\  & Privacy and Security & [42] & Discuss data and model poisoning backdoor attacks and defenses for FL. \\ \hline \multirow{4}{*}{SL} & Edge Computing & [19] & Compare SL and FL methods for the Internet of Things. \\  & Privacy and Security & [20] & Discuss the issues about data leakage and protection in SL. \\ \cline{1-1}  & Privacy and Security & [21] & Evaluate the robustness of SL paradigms against model inversion attacks. \\ \cline{1-1} \cline{2-5}  & Concept and Method; & \multirow{2}{*}{This Work} & Review SL concepts and paradigms; Compare the performance \\  & Evaluation & & of different SL methods. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of surveys and experimental evaluations on FL and SL topics. * Lack of SL Benchmarks and Evaluations: Recent years have witnessed rapid advancements in SL. However, the lack of benchmarks and evaluations has made it difficult to fairly compare the performance of SL paradigms. Existing SL paradigms are often evaluated in datasets from various domains, including vision [13], healthcare [14], and finance [44]. Furthermore, data partitioning methods may vary from one study to another. For example, for SL in the non-IID setting, some studies divide the dataset based on the labels of samples, while others utilize the Dirichlet distribution. A few existing studies do not even evaluate the performance of their paradigms on non-IID data. Therefore, in Table 2, we first summarize the main datasets used in recent SL studies and the partitioning methods on these datasets. In this paper, we provide a systematic survey and comparison of the existing SL paradigms. We first classify existing SL paradigms along different dimensions and provide a detailed discussion on each of them. Our selection criteria focus on whether the SL paradigm demonstrates effective solutions in reducing communication and computation costs, ensuring label protection and addressing data and computation-communication heterogeneity. To comprehensively evaluate the performance of these SL paradigms, we performed extensive experiments on four widely used datasets that cover a wide range of tasks including image classification, medical data analysis, tabular prediction, and drug discovery in both IID and non-IID settings. By analyzing the experimental results, we provide extensive engineering advice for ML developers to choose appropriate SL paradigms according to their specific needs. We also provide research insights on the robustness and efficiency of existing SL paradigms for future improvements. The main contributions of this paper are summarized as follows:  
* Systematic Review of Existing SL Paradigms: We review existing SL paradigms and classify them according to their characteristics.