Fed-ensemble: Ensemble Models in Federated Learning for Improved Generalization and Uncertainty Quantification

The increase in the computational power of edge devices has opened up the possibility of processing some of the data at the edge and distributing model learning. This paradigm is often called federated learning (FL), where edge devices exploit their local computational resources to train models collaboratively. Though FL has seen recent success, it is unclear how to characterize uncertainties in FL predictions. In this paper, we propose
Fed-ensemble
: a simple approach that brings model ensembling to FL. Instead of aggregating local models 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 compared with single-model methods. 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. Also, by carefully choosing client-dependent weights in the inference stage,
Fed-ensemble
becomes personalized and yields even better performance. Theoretically, we show that predictions on new data from all
$K$
models belong to the same predictive posterior distribution under a neural tangent kernel regime. This result, in turn, sheds light on the generalization advantages of model averaging and justifies the uncertainty quantification capability. We also illustrate that
Fed-ensemble
has an elegant Bayesian interpretation.
Note to Practitioners
—provides an algorithm that extracts a set of
$K$
solutions without imposing any additional communication overhead in FL. Given multiple solutions,
Fed-ensemble
can be exploited to personalize inference as well as quantify uncertainty. Such capabilities may be beneficial within multiple practical systems that require uncertainty-aware decision-making. Further,
Fed-ensemble
may be useful for model validation and hypothesis testing.