  This paper aims to establish a new optimization paradigm for implementing
realistic distributed learning algorithms, with performance guarantees, on
wireless edge nodes with heterogeneous computing and communication capacities.
We will refer to this new paradigm as `Mobile Edge Learning (MEL)'. The problem
of dynamic task allocation for MEL is considered in this paper with the aim to
maximize the learning accuracy, while guaranteeing that the total times of data
distribution/aggregation over heterogeneous channels, and local computing
iterations at the heterogeneous nodes, are bounded by a preset duration. The
problem is first formulated as a quadratically-constrained integer linear
problem. Being an NP-hard problem, the paper relaxes it into a non-convex
problem over real variables. We thus proposed two solutions based on deriving
analytical upper bounds of the optimal solution of this relaxed problem using
Lagrangian analysis and KKT conditions, and the use of suggest-and-improve
starting from equal batch allocation, respectively. The merits of these
proposed solutions are exhibited by comparing their performances to both
numerical approaches and the equal task allocation approach.
