  Federated learning (FL) has emerged as an effective approach to address
consumer privacy needs. FL has been successfully applied to certain machine
learning tasks, such as training smart keyboard models and keyword spotting.
Despite FL's initial success, many important deep learning use cases, such as
ranking and recommendation tasks, have been limited from on-device learning.
One of the key challenges faced by practical FL adoption for DL-based ranking
and recommendation is the prohibitive resource requirements that cannot be
satisfied by modern mobile systems. We propose Federated Ensemble Learning
(FEL) as a solution to tackle the large memory requirement of deep learning
ranking and recommendation tasks. FEL enables large-scale ranking and
recommendation model training on-device by simultaneously training multiple
model versions on disjoint clusters of client devices. FEL integrates the
trained sub-models via an over-arch layer into an ensemble model that is hosted
on the server. Our experiments demonstrate that FEL leads to 0.43-2.31% model
quality improvement over traditional on-device federated learning - a
significant improvement for ranking and recommendation system use cases.
