  To leverage data and computation capabilities of mobile devices, machine
learning algorithms are deployed at the network edge for training artificial
intelligence (AI) models, resulting in the new paradigm of edge learning. In
this paper, we consider the framework of partitioned edge learning for
iteratively training a large-scale model using many resource-constrained
devices (called workers). To this end, in each iteration, the model is
dynamically partitioned into parametric blocks, which are downloaded to worker
groups for updating using data subsets. Then, the local updates are uploaded to
and cascaded by the server for updating a global model. To reduce resource
usage by minimizing the total learning-and-communication latency, this work
focuses on the novel joint design of parameter (computation load) allocation
and bandwidth allocation (for downloading and uploading). Two design approaches
are adopted. First, a practical sequential approach, called partially
integrated parameter-and-bandwidth allocation (PABA), yields two schemes,
namely bandwidth aware parameter allocation and parameter aware bandwidth
allocation. The former minimizes the load for the slowest (in computing) of
worker groups, each training a same parametric block. The latter allocates the
largest bandwidth to the worker being the latency bottleneck. Second, PABA are
jointly optimized. Despite its being a nonconvex problem, an efficient and
optimal solution algorithm is derived by intelligently nesting a bisection
search and solving a convex problem. Experimental results using real data
demonstrate that integrating PABA can substantially improve the performance of
partitioned edge learning in terms of latency (by e.g., 46%) and accuracy (by
e.g., 4%).
