  Federated learning has emerged as an innovative paradigm of collaborative
machine learning. Unlike conventional machine learning, a global model is
collaboratively learned while data remains distributed over a tremendous number
of client devices, thus not compromising user privacy. However, several
challenges still remain despite its glowing popularity; above all, the global
aggregation in federated learning involves the challenge of biased model
averaging and lack of prior knowledge in client sampling, which, in turn, leads
to high generalization error and slow convergence rate, respectively. In this
work, we propose a novel algorithm called FedCM that addresses the two
challenges by utilizing prior knowledge with multi-armed bandit based client
sampling and filtering biased models with combinatorial model averaging. Based
on extensive evaluations using various algorithms and representative
heterogeneous datasets, we showed that FedCM significantly outperformed the
state-of-the-art algorithms by up to 37.25% and 4.17 times, respectively, in
terms of generalization accuracy and convergence rate.
