  Split learning (SL) is a privacy-preserving distributed deep learning method
used to train a collaborative model without the need for sharing of patient's
raw data between clients. In split learning, an additional privacy-preserving
algorithm called no-peek algorithm can be incorporated, which is robust to
adversarial attacks. The privacy benefits offered by split learning make it
suitable for practice in the healthcare domain. However, the split learning
algorithm is flawed as the collaborative model is trained sequentially, i.e.,
one client trains after the other. We point out that the model trained using
the split learning algorithm gets biased towards the data of the clients used
for training towards the end of a round. This makes SL algorithms highly
susceptible to the order in which clients are considered for training. We
demonstrate that the model trained using the data of all clients does not
perform well on the client's data which was considered earliest in a round for
training the model. Moreover, we show that this effect becomes more prominent
with the increase in the number of clients. We also demonstrate that the
SplitFedv3 algorithm mitigates this problem while still leveraging the privacy
benefits provided by split learning.
