  Federated Learning (FL) trains a machine learning model on distributed
clients without exposing individual data. Unlike centralized training that is
usually based on carefully-organized data, FL deals with on-device data that
are often unfiltered and imbalanced. As a result, conventional FL training
protocol that treats all data equally leads to a waste of local computational
resources and slows down the global learning process. To this end, we propose
FedBalancer, a systematic FL framework that actively selects clients' training
samples. Our sample selection strategy prioritizes more "informative" data
while respecting privacy and computational capabilities of clients. To better
utilize the sample selection to speed up global training, we further introduce
an adaptive deadline control scheme that predicts the optimal deadline for each
round with varying client training data. Compared with existing FL algorithms
with deadline configuration methods, our evaluation on five datasets from three
different domains shows that FedBalancer improves the time-to-accuracy
performance by 1.20~4.48x while improving the model accuracy by 1.1~5.0%. We
also show that FedBalancer is readily applicable to other FL approaches by
demonstrating that FedBalancer improves the convergence speed and accuracy when
operating jointly with three different FL algorithms.
