  A key functionality of emerging connected autonomous systems such as smart
transportation systems, smart cities, and the industrial Internet-of-Things, is
the ability to process and learn from data collected at different physical
locations. This is increasingly attracting attention under the terms of
distributed learning and federated learning. However, in this setup data
transfer takes place over communication resources that are shared among many
users and tasks or subject to capacity constraints. This paper examines
algorithms for efficiently allocating resources to linear regression tasks by
exploiting the informativeness of the data. The algorithms developed enable
adaptive scheduling of learning tasks with reliable performance guarantees.
