  The rapid development of artificial intelligence together with the powerful
computation capabilities of the advanced edge servers make it possible to
deploy learning tasks at the wireless network edge, which is dubbed as edge
intelligence (EI). The communication bottleneck between the data resource and
the server results in deteriorated learning performance as well as tremendous
energy consumption. To tackle this challenge, we explore a new paradigm called
learning-and-energy-efficient (LEE) EI, which simultaneously maximizes the
learning accuracies and energy efficiencies of multiple tasks via data
partition and rate control. Mathematically, this results in a multi-objective
optimization problem. Moreover, the continuous varying rates over the whole
transmission duration introduce infinite variables. To solve this complex
problem, we consider the case with infinite server buffer capacity and one-shot
data arrival at sensor. First, the number of variables are reduced to a finite
level by exploiting the optimality of constant-rate transmission in each epoch.
Second, the optimal solution is found by applying stratified sequencing or
objectives merging. By assuming higher priority of learning efficiency in
stratified sequencing, the closed form of optimal data partition is derived by
the Lagrange method, while the optimal rate control is proved to have the
structure of directional water filling (DWF), based on which a string-pulling
(SP) algorithm is proposed to obtain the numerical values. The DWF structure of
rate control is also proved to be optimal in objectives merging via weighted
summation. By exploiting the optimal rate changing properties, the SP algorithm
is further extended to account for the cases with limited server buffer
capacity or bursty data arrival at sensor. The performance of the proposed
design is examined by extensive experiments based on public datasets.
