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.

在物联网时代，分散的机器学习范式变得越来越重要。在本文中，我们介绍了一种利用设备模型权重之间的欧氏距离来评估它们的相似性和差异性的联邦学习模型。这对于我们的系统是基础性的，它根据模型权重的接近程度指导设备之间的联盟形成。此外，一个代表模型权重平均值的重心的概念有助于从多个设备合并更新。我们使用均匀和异构数据分布来评估我们的方法，并将其与传统的联邦学习平均算法进行比较。数值结果证明了它在为基于物联网的机器学习提供结构化、优越和通信高效的模型方面的潜力。