  In some applications, edge learning is experiencing a shift in focusing from
conventional learning from scratch to new two-stage learning unifying
pre-training and task-specific fine-tuning. This paper considers the problem of
joint communication and computation resource management in a two-stage edge
learning system. In this system, model pre-training is first conducted at an
edge server via centralized learning on local pre-stored general data, and then
task-specific fine-tuning is performed at edge devices based on the pre-trained
model via federated edge learning. For the two-stage learning model, we first
analyze the convergence behavior (in terms of the average squared gradient norm
bound), which characterizes the impacts of various system parameters such as
the number of learning rounds and batch sizes in the two stages on the
convergence rate. Based on our analytical results, we then propose a joint
communication and computation resource management design to minimize an average
squared gradient norm bound, subject to constraints on the transmit power,
overall system energy consumption, and training delay. The decision variables
include the number of learning rounds, batch sizes, clock frequencies, and
transmit power control for both pre-training and fine-tuning stages. Finally,
numerical results are provided to evaluate the effectiveness of our proposed
design. It is shown that the proposed joint resource management over the
pre-training and fine-tuning stages well balances the system performance
trade-off among the training accuracy, delay, and energy consumption. The
proposed design is also shown to effectively leverage the inherent trade-off
between pre-training and fine-tuning, which arises from the differences in data
distribution between pre-stored general data versus real-time task-specific
data, thus efficiently optimizing overall system performance.
