  Federated Learning (FL), Split Learning (SL), and SplitFed Learning (SFL) are
three recent developments in distributed machine learning that are gaining
attention due to their ability to preserve the privacy of raw data. Thus, they
are widely applicable in various domains where data is sensitive, such as
large-scale medical image classification, internet-of-medical-things, and
cross-organization phishing email detection. SFL is developed on the confluence
point of FL and SL. It brings the best of FL and SL by providing parallel
client-side machine learning model updates from the FL paradigm and a higher
level of model privacy (while training) by splitting the model between the
clients and server coming from SL. However, SFL has communication and
computation overhead at the client-side due to the requirement of client-side
model synchronization. For the resource-constrained client-side, removal of
such requirements is required to gain efficiency in the learning. In this
regard, this paper studies SFL without client-side model synchronization. The
resulting architecture is known as Multi-head Split Learning. Our empirical
studies considering the ResNet18 model on MNIST data under IID data
distribution among distributed clients find that Multi-head Split Learning is
feasible. Its performance is comparable to the SFL. Moreover, SFL provides only
1%-2% better accuracy than Multi-head Split Learning on the MNIST test set. To
further strengthen our results, we study the Multi-head Split Learning with
various client-side model portions and its impact on the overall performance.
To this end, our results find a minimal impact on the overall performance of
the model.
