Under the federated learning paradigm, a set of nodes can cooperatively train a machine learning model with the help of a centralized server. Such a server is also tasked with assigning a weight to the information received from each node, and often also to drop too-slow nodes from the learning process. Both decisions have major impact on the resulting learning performance, and can interfere with each other in counter-in-tuitive ways. In this article, we focus on edge networking scenarios and investigate existing and novel approaches to such model-weighting and node-dropping decisions. Leveraging a set of realworld experiments, we find that popular, straightforward decision making approaches may yield poor performance, and that considering the quality of data in addition to its quantity can substantially improve learning.

中文翻译：

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网络边缘的联邦学习：并非所有节点都是平等创建的


在联邦学习范式下，一组节点可以在集中式服务器的帮助下协作训练机器学习模型。这样的服务器还负责为从每个节点接收到的信息分配权重，并且通常还从学习过程中删除太慢的节点。这两个决定都会对最终的学习成绩产生重大影响，并且可能会以违反直觉的方式相互干扰。在本文中，我们重点关注边缘网络场景，并研究此类模型加权和节点丢弃决策的现有方法和新颖方法。利用一组现实世界的实验，我们发现流行的、直接的决策方法可能会产生较差的性能，而除了数量之外考虑数据的质量可以极大地改善学习。