# A Review and Experimental Evaluation on Split Learning
## 1 Introduction  
Nowadays, a lot of edge devices, including sensors, smartphones, and wearables, are producing huge volumes of valuable data. Such data can be used to collectively train high-quality Deep Learning (DL) models through edge computing [1], which have broad real-world applications in various domains, including Industry 4.0 [2], healthcare [3], fintech [4], transportation [5], and energy [6]. However, these data are usually owned by organizations whose regulations do not allow for data sharing and exchange. For example, the exchange of sensitive raw patient data between healthcare providers for medical analysis can cause serious privacy concerns [7, 8], thus having been prohibited by laws for privacy protection [9]. In such scenarios, data are scattered across different silos or edge devices and cannot be collected on a central server for the training of DL models. To address the above problems, various schemes have been proposed to enable collaborative DL model training on decentralized silos or devices without raw data sharing and exchange [10, 11]. Among these frameworks, Federated Learning (FL) [12] and Split Learning (SL) [13, 14] are the most prominent. FL allows different clients to collaboratively train a shared DL model while keeping all training data locally and only exchanging gradients to ensure privacy. However, due to the demands of real-world applications, DL models have become larger and more complex to achieve satisfactory performance. Despite the increasing computational capabilities of modern hardware, training large-scale DL models on resource-constrained edge devices, such as sensors, smartphones, and wearables [15],remains a great challenge. Consequently, SL is introduced as an alternative to FL for resource-constrained collaborative training, where a set of participants (i.e., clients) with limited resources can train a large-scale DL model with the assistance of a supercomputer center (i.e., server). The core idea of SL is to divide a large model into smaller client-side models and a server-side model. In this way, clients only perform lightweight computations and transmit intermediate-layer activations, rather than raw data, to the server to meet data regulatory requirements. Therefore, the key distinction between SL and FL is that clients hold the complete model in FL, whereas clients hold only a part of the complete model in SL. In recent years, there have been several surveys and experimental evaluations for FL, as summarized in Table 1. These studies have covered multiple aspects of FL, including but not limited to types and methods of FL, privacy and security issues in FL, FL in edge environments, the design of incentive mechanisms, model aggregation and client selection methods for FL, and the applications of FL in healthcare, industrial engineering, intrusion detection, and other domains. However, although SL has also been extensively studied and there have been a variety of SL paradigms [13; 16; 17; 18] in the literature, to the best of our knowledge, there are only a few surveys and evaluations for SL [19; 20; 21]. Furthermore, the surveys in [20; 21] are specific to security and privacy issues in SL and do not provide comprehensive discussions and evaluations of SL paradigms. The evaluations in [19], which were published in 2020, focus more on FL than SL and do not compare the more recent FL and SL paradigms. Therefore, several critical problems for SL research have not yet been well addressed, as listed below. * Lack of Systematic Review and Categorization of SL Paradigms: SL paradigms are highly diverse in terms of label sharing, model aggregation, cut layer selection, etc. There has not been a systematic review and categorization of the existing SL paradigms. In particular, paradigms that combine FL with SL or even obscure the distinctions between Vertical FL (VFL) [43] and SL cannot be precisely categorized in the existing literature. This poses challenges for researchers to compare SL paradigms, who often find themselves having to reinvent the wheel.