Federated Learning (FL) is a paradigm that aims to support loosely connected clients in learning a global model collaboratively with the help of a centralized server. The most popular FL algorithm is Federated Averaging (FedAvg), which is based on taking weighted average of the client models, with the weights determined largely based on dataset sizes at the clients. In this paper, we propose a new approach, termed Federated Node Selection (FedNS), for the server's global model aggregation in the FL setting. FedNS filters and re-weights the clients' models at the node/kernel level, hence leading to a potentially better global model by fusing the best components of the clients. Using collaborative image classification as an example, we show with experiments from multiple datasets and networks that FedNS can consistently achieve improved performance over FedAvg.

中文翻译：

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FedNS：改进联合学习以在移动客户端上进行协作图像分类

联合学习（FL）是一种范例，旨在支持松散连接的客户端在中央服务器的帮助下协作学习全局模型。最受欢迎的FL算法是联合平均（FedAvg），该算法基于对客户端模型的加权平均，而权重很大程度上取决于客户端的数据集大小。在本文中，我们为FL设置中的服务器全局模型聚合提出了一种称为联合节点选择（FedNS）的新方法。FedNS在节点/内核级别对客户端的模型进行过滤和重新加权，因此通过融合客户端的最佳组件，可以潜在地改善全局模型。以协作图像分类为例，