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.

本文提出 Fed-ensemble: 一种将模型集成应用于联邦学习的简单方法，其通过随机排列更新一组 K 个模型，然后通过模型平均获得预测结果，证明了在神经切线核环境下新数据的预测来自所有 K 个模型的相同预测后验分布，并通过实验证明其在联邦学习算法中具有出色的性能，特别是在常见的异构设置中表现突出。