  Split learning (SL) is an emergent distributed learning framework which can
mitigate the computation and wireless communication overhead of federated
learning. It splits a machine learning model into a device-side model and a
server-side model at a cut layer. Devices only train their allocated model and
transmit the activations of the cut layer to the server. However, SL can lead
to data leakage as the server can reconstruct the input data using the
correlation between the input and intermediate activations. Although allocating
more layers to a device-side model can reduce the possibility of data leakage,
this will lead to more energy consumption for resource-constrained devices and
more training time for the server. Moreover, non-iid datasets across devices
will reduce the convergence rate leading to increased training time. In this
paper, a new personalized SL framework is proposed. For this framework, a novel
approach for choosing the cut layer that can optimize the tradeoff between the
energy consumption for computation and wireless transmission, training time,
and data privacy is developed. In the considered framework, each device
personalizes its device-side model to mitigate non-iid datasets while sharing
the same server-side model for generalization. To balance the energy
consumption for computation and wireless transmission, training time, and data
privacy, a multiplayer bargaining problem is formulated to find the optimal cut
layer between devices and the server. To solve the problem, the
Kalai-Smorodinsky bargaining solution (KSBS) is obtained using the bisection
method with the feasibility test. Simulation results show that the proposed
personalized SL framework with the cut layer from the KSBS can achieve the
optimal sum utilities by balancing the energy consumption, training time, and
data privacy, and it is also robust to non-iid datasets.
