  Federated Learning (FL) has shown great potential as a privacy-preserving
solution to learning from decentralized data which are only accessible locally
on end devices (i.e., clients). In many scenarios, however, a large proportion
of the clients are probably in possession of only low-quality data that are
biased, noisy or even irrelevant. As a result, they could significantly degrade
the quality of the global model we aim to build and slow down its convergence
in the course of FL. In light of this, we propose a novel approach to
optimizing FL under such circumstances without breaching data privacy. The key
of our approach is a dynamic data profiling method for generating model-data
footprints on each client and the server. The footprint encodes the
representation of the global model on the corresponding data partition based on
the output distribution of the model's first fully-connected layer (FC-1). By
matching the footprints from clients and the server, we adaptively adjust each
client's opportunity of participation in each FL round to mitigate the impact
from the clients with low-quality data. We have conducted extensive experiments
on public data sets using various FL settings. Results show that our method
significantly reduces the number of rounds (by up to 75\%) and overall time (by
up to 68\%) required to have the global model converge whiling increasing the
global model's accuracy by up to 2.5\%.
