Federated learning (FL) and split learning (SL) are two popular distributed machine learning approaches. Both follow a model-to-data scenario; clients train and test machine learning models without sharing raw data. SL provides better model privacy than FL due to the machine learning model architecture split between clients and the server. Moreover, the split model makes SL a better option for resource-constrained environments. However, SL performs slower than FL due to the relay-based training across multiple clients. In this regard, this paper presents a novel approach, named splitfed learning (SFL), that amalgamates the two approaches eliminating their inherent drawbacks, along with a refined architectural configuration incorporating differential privacy and PixelDP to enhance data privacy and model robustness. Our analysis and empirical results demonstrate that (pure) SFL provides similar test accuracy and communication efficiency as SL while significantly decreasing its computation time per global epoch than in SL for multiple clients. Furthermore, as in SL, its communication efficiency over FL improves with the number of clients. Besides, the performance of SFL with privacy and robustness measures is further evaluated under extended experimental settings.
翻译:联邦学习(FL)和分解学习(SL)是两种流行的分布式机器学习方法,两者都遵循模型到数据的设想;客户培训和测试机器学习模式,但不分享原始数据;由于客户和服务器之间的机器学习模式结构分离,SL提供比FL更好的模型隐私;此外,分解模式使SL成为资源受限制环境的更好选择;然而,由于多个客户的中继培训,SL的表现比FL慢;在这方面,本文件介绍了一种新颖的方法,称为分解学习(SFL),将两种方法合并起来,消除其固有的缺陷,同时完善建筑结构配置,包括不同的隐私和PixelDP,以加强数据隐私和模型稳健性;我们的分析和经验结果显示,SLFL提供与SL相似的测试准确性和通信效率,同时大大缩短了多个客户的全球百分点计算时间;此外,在SL,与客户人数相比,SL一样,SL的通信效率有所提高。此外,在扩展的实验环境中,SL的隐私和稳健度措施的业绩得到了进一步评价。