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.

翻译：在物联网时代，去中心化的机器学习范式正日益受到重视。本文提出一种联邦学习模型，该模型利用设备模型权重之间的欧氏距离来评估其相似性与差异性。这一机制构成了我们系统的基础，根据设备模型权重的接近程度指导设备间联盟的形成。此外，通过引入代表模型权重平均值的重心概念，有助于聚合来自多个设备的更新信息。我们采用同质与异质数据分布对所提方法进行评估，并将其与传统联邦学习平均算法进行对比。数值结果表明，该模型在构建结构化的物联网机器学习模型方面具有潜力，且其性能更优、通信效率更高。