  The problem of learning simultaneously several related tasks has received
considerable attention in several domains, especially in machine learning with
the so-called multitask learning problem or learning to learn problem [1], [2].
Multitask learning is an approach to inductive transfer learning (using what is
learned for one problem to assist in another problem) and helps improve
generalization performance relative to learning each task separately by using
the domain information contained in the training signals of related tasks as an
inductive bias. Several strategies have been derived within this community
under the assumption that all data are available beforehand at a fusion center.
However, recent years have witnessed an increasing ability to collect data in a
distributed and streaming manner. This requires the design of new strategies
for learning jointly multiple tasks from streaming data over distributed (or
networked) systems. This article provides an overview of multitask strategies
for learning and adaptation over networks. The working hypothesis for these
strategies is that agents are allowed to cooperate with each other in order to
learn distinct, though related tasks. The article shows how cooperation steers
the network limiting point and how different cooperation rules allow to promote
different task relatedness models. It also explains how and when cooperation
over multitask networks outperforms non-cooperative strategies.
