  Federated Learning (FL) has shown great potential as a privacy-preserving
solution to learning from decentralized data that are only accessible to end
devices (i.e., clients). In many scenarios, however, a large proportion of the
clients are probably in possession of low-quality data that are biased, noisy
or even irrelevant. As a result, they could significantly slow down the
convergence of the global model we aim to build and also compromise its
quality. In light of this, we propose FedProf, a novel algorithm for optimizing
FL under such circumstances without breaching data privacy. The key of our
approach is a distributional representation profiling and matching scheme that
uses the global model to dynamically profile data representations and allows
for low-cost, lightweight representation matching. Based on the scheme we
adaptively score each client and adjust its participation probability so as to
mitigate the impact of low-value clients on the training process. We have
conducted extensive experiments on public datasets using various FL settings.
The results show that the selective behaviour of our algorithm leads to a
significant reduction in the number of communication rounds and the amount of
time (up to 2.4x speedup) for the global model to converge and also provides
accuracy gain.
