  Split learning (SL) has been recently proposed as a way to enable
resource-constrained devices to train multi-parameter neural networks (NNs) and
participate in federated learning (FL). In a nutshell, SL splits the NN model
into parts, and allows clients (devices) to offload the largest part as a
processing task to a computationally powerful helper. In parallel SL, multiple
helpers can process model parts of one or more clients, thus, considerably
reducing the maximum training time over all clients (makespan). In this paper,
we focus on orchestrating the workflow of this operation, which is critical in
highly heterogeneous systems, as our experiments show. In particular, we
formulate the joint problem of client-helper assignments and scheduling
decisions with the goal of minimizing the training makespan, and we prove that
it is NP-hard. We propose a solution method based on the decomposition of the
problem by leveraging its inherent symmetry, and a second one that is fully
scalable. A wealth of numerical evaluations using our testbed's measurements
allow us to build a solution strategy comprising these methods. Moreover, we
show that this strategy finds a near-optimal solution, and achieves a shorter
makespan than the baseline scheme by up to 52.3%.
