  Data privacy concerns has made centralized training of data, which is
scattered across silos, infeasible, leading to the need for collaborative
learning frameworks. To address that, two prominent frameworks emerged, i.e.,
federated learning (FL) and split learning (SL). While FL has established
various benchmark frameworks and research libraries,SL currently lacks a
unified library despite its diversity in terms of label sharing, model
aggregation, and cut layer choice. This lack of standardization makes comparing
SL paradigms difficult. To address this, we propose SLPerf, a unified research
framework and open research library for SL, and conduct extensive experiments
on four widely-used datasets under both IID and Non-IID data settings. Our
contributions include a comprehensive survey of recently proposed SL paradigms,
a detailed benchmark comparison of different SL paradigms in different
situations, and rich engineering take-away messages and research insights for
improving SL paradigms. SLPerf can facilitate SL algorithm development and fair
performance comparisons. The code is available at
https://github.com/Rainysponge/Split-learning-Attacks .
