In a decentralized machine learning system, data is typically partitioned among multiple devices or nodes, each of which trains a local model using its own data. These local models are then shared and combined to create a global model that can make accurate predictions on new data. In this paper, we start exploring the role of the network topology connecting nodes on the performance of a Machine Learning model trained through direct collaboration between nodes. We investigate how different types of topologies impact the "spreading of knowledge", i.e., the ability of nodes to incorporate in their local model the knowledge derived by learning patterns in data available in other nodes across the networks. Specifically, we highlight the different roles in this process of more or less connected nodes (hubs and leaves), as well as that of macroscopic network properties (primarily, degree distribution and modularity). Among others, we show that, while it is known that even weak connectivity among network components is sufficient for information spread, it may not be sufficient for knowledge spread. More intuitively, we also find that hubs have a more significant role than leaves in spreading knowledge, although this manifests itself not only for heavy-tailed distributions but also when "hubs" have only moderately more connections than leaves. Finally, we show that tightly knit communities severely hinder knowledge spread.

翻译：在去中心化机器学习系统中，数据通常分布在多个设备或节点上，每个节点使用自身数据训练本地模型。这些本地模型随后被共享并组合以生成一个能对新数据进行准确预测的全局模型。本文初步探索了连接节点的网络拓扑结构对通过节点间直接协作训练的机器学习模型性能的影响。我们研究了不同类型的拓扑如何影响"知识传播"，即节点将网络中其他节点通过学习数据模式所获得的知识融入自身本地模型的能力。具体而言，我们重点分析了连接程度不同的节点（枢纽节点与叶节点）以及网络宏观属性（主要是度分布和模块度）在此过程中的不同作用。研究发现：尽管已知网络组件间即使存在弱连接也足以实现信息传播，但这未必足以支撑知识传播；更为直观的是，枢纽节点在知识传播中的作用显著大于叶节点——这种差异不仅体现在重尾分布场景下，甚至在"枢纽"仅比叶节点拥有适度更多连接时同样存在。最后，我们证明了紧密关联的社区会严重阻碍知识传播。