  Nowadays, data-driven, machine and deep learning approaches have provided
unprecedented performance in various complex tasks, including image
classification and object detection, and in a variety of application areas,
like autonomous vehicles, medical imaging and wireless communications.
Traditionally, such approaches have been deployed, along with the involved
datasets, on standalone devices. Recently, a shift has been observed towards
the so-called Edge Machine Learning, in which centralized architectures are
adopted that allow multiple devices with local computational and storage
resources to collaborate with the assistance of a centralized server. The
well-known federated learning approach is able to utilize such architectures by
allowing the exchange of only parameters with the server, while keeping the
datasets private to each contributing device. In this work, we propose a fully
distributed, diffusion-based learning algorithm that does not require a central
server and propose an adaptive combination rule for the cooperation of the
devices. By adopting a classification task on the MNIST dataset, the efficacy
of the proposed algorithm over corresponding counterparts is demonstrated via
the reduction of the number of collaboration rounds required to achieve an
acceptable accuracy level in non- IID dataset scenarios.
