SLPerf: a Unified Framework for Benchmarking Split Learning
This work addresses the problem of comparing and developing SL algorithms for researchers and practitioners, but it is incremental as it builds on existing SL concepts without introducing new methods.
The authors tackled the lack of standardization in split learning (SL) by proposing SLPerf, a unified framework and open library for benchmarking SL paradigms, conducting experiments on four datasets under IID and Non-IID settings.
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 .