  In this paper, we consider partitioned edge learning (PARTEL), which
implements parameter-server training, a well known distributed learning method,
in a wireless network. Thereby, PARTEL leverages distributed computation
resources at edge devices to train a large-scale artificial intelligence (AI)
model by dynamically partitioning the model into parametric blocks for
separated updating at devices. Targeting broadband channels, we consider the
joint control of parameter allocation, sub-channel allocation, and transmission
power to improve the performance of PARTEL. Specifically, the policies for
joint SUbcarrier, Parameter, and POweR allocaTion (SUPPORT) are optimized under
the criterion of minimum learning latency. Two cases are considered. First, for
the case of decomposable models (e.g., logistic regression), the
latency-minimization problem is a mixed-integer program and non-convex. Due to
its intractability, we develop a practical solution by integer relaxation and
transforming it into an equivalent convex problem of model size maximization
under a latency constraint. Thereby, a low-complexity algorithm is designed to
compute the SUPPORT policy. Second, consider the case of deep neural network
(DNN) models which can be trained using PARTEL by introducing some auxiliary
variables. This, however, introduces constraints on model partitioning reducing
the granularity of parameter allocation. The preceding policy is extended to
DNN models by applying the proposed techniques of load rounding and
proportional adjustment to rein in latency expansion caused by the load
granularity constraints.
