  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.
