  The traditional framework of federated learning (FL) requires each client to
re-train their models in every iteration, making it infeasible for
resource-constrained mobile devices to train deep-learning (DL) models. Split
learning (SL) provides an alternative by using a centralized server to offload
the computation of activations and gradients for a subset of the model but
suffers from problems of slow convergence and lower accuracy. In this paper, we
implement PFSL, a new framework of distributed split learning where a large
number of thin clients perform transfer learning in parallel, starting with a
pre-trained DL model without sharing their data or labels with a central
server. We implement a lightweight step of personalization of client models to
provide high performance for their respective data distributions. Furthermore,
we evaluate performance fairness amongst clients under a work fairness
constraint for various scenarios of non-i.i.d. data distributions and unequal
sample sizes. Our accuracy far exceeds that of current SL algorithms and is
very close to that of centralized learning on several real-life benchmarks. It
has a very low computation cost compared to FL variants and promises to deliver
the full benefits of DL to extremely thin, resource-constrained clients.
