  In this paper, we introduce the concept of collective learning (CL) which
exploits the notion of collective intelligence in the field of distributed
semi-supervised learning. The proposed framework draws inspiration from the
learning behavior of human beings, who alternate phases involving
collaboration, confrontation and exchange of views with other consisting of
studying and learning on their own. On this regard, CL comprises two main
phases: a self-training phase in which learning is performed on local private
(labeled) data only and a collective training phase in which proxy-labels are
assigned to shared (unlabeled) data by means of a consensus-based algorithm. In
the considered framework, heterogeneous systems can be connected over the same
network, each with different computational capabilities and resources and
everyone in the network may take advantage of the cooperation and will
eventually reach higher performance with respect to those it can reach on its
own. An extensive experimental campaign on an image classification problem
emphasizes the properties of CL by analyzing the performance achieved by the
cooperating agents.
