Fed-ensemble: Improving Generalization through Model Ensembling in Federated Learning

• URL: http://arxiv.org/abs/2107.10663v1
• Date: Wed, 21 Jul 2021 14:40:14 GMT
• Title: Fed-ensemble: Improving Generalization through Model Ensembling in Federated Learning
• Authors: Naichen Shi, Fan Lai, Raed Al Kontar, Mosharaf Chowdhury
• Abstract summary: Fed-ensemble brings model ensembling to federated learning (FL) Fed-ensemble can be readily utilized within established FL methods.
• Score: 5.882234707363695
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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