In this paper we propose Fed-ensemble: a simple approach that bringsmodel ensembling to federated learning (FL). Instead of aggregating localmodels to update a single global model, Fed-ensemble uses random permutations to update a group of K models and then obtains predictions through model averaging. Fed-ensemble can be readily utilized within established FL methods and does not impose a computational overhead as it only requires one of the K models to be sent to a client in each communication round. Theoretically, we show that predictions on newdata from all K models belong to the same predictive posterior distribution under a neural tangent kernel regime. This result in turn sheds light onthe generalization advantages of model averaging. We also illustrate thatFed-ensemble has an elegant Bayesian interpretation. Empirical results show that our model has superior performance over several FL algorithms,on a wide range of data sets, and excels in heterogeneous settings often encountered in FL applications.

翻译：在本文中,我们提出了美联储的组合:一种简单的方法,将模型集合到联合学习(FL)中。 美联储的组合使用随机的变相来更新一组K型模型,然后通过平均模型获得预测。 美联储的组合可以很容易地在既定的FL方法中使用,而不会强加计算性间接费用,因为它仅仅要求在每轮通信中将K型模型之一发送给客户。理论上,我们显示所有K型模型的新数据的预测都属于同一神经对流内核系统下的预测远地点分布。这反过来又揭示了模型平均的通用优势。 我们还说明,Fed-组合有一个优雅的贝叶斯语解释。 经验性结果显示,我们的模型在多种FL算法上表现优于多个FL算法,涉及广泛的数据集,以及在FL应用中经常遇到的多元环境中的优异特性。