  In the era of the Internet of Things (IoT), decentralized paradigms for
machine learning are gaining prominence. In this paper, we introduce a
federated learning model that capitalizes on the Euclidean distance between
device model weights to assess their similarity and disparity. This is
foundational for our system, directing the formation of coalitions among
devices based on the closeness of their model weights. Furthermore, the concept
of a barycenter, representing the average of model weights, helps in the
aggregation of updates from multiple devices. We evaluate our approach using
homogeneous and heterogeneous data distribution, comparing it against
traditional federated learning averaging algorithm. Numerical results
demonstrate its potential in offering structured, outperformed and
communication-efficient model for IoT-based machine learning.
