  By exploiting the computing power and local data of distributed clients,
federated learning (FL) features ubiquitous properties such as reduction of
communication overhead and preserving data privacy. In each communication round
of FL, the clients update local models based on their own data and upload their
local updates via wireless channels. However, latency caused by hundreds to
thousands of communication rounds remains a bottleneck in FL. To minimize the
training latency, this work provides a multi-armed bandit-based framework for
online client scheduling (CS) in FL without knowing wireless channel state
information and statistical characteristics of clients. Firstly, we propose a
CS algorithm based on the upper confidence bound policy (CS-UCB) for ideal
scenarios where local datasets of clients are independent and identically
distributed (i.i.d.) and balanced. An upper bound of the expected performance
regret of the proposed CS-UCB algorithm is provided, which indicates that the
regret grows logarithmically over communication rounds. Then, to address
non-ideal scenarios with non-i.i.d. and unbalanced properties of local datasets
and varying availability of clients, we further propose a CS algorithm based on
the UCB policy and virtual queue technique (CS-UCB-Q). An upper bound is also
derived, which shows that the expected performance regret of the proposed
CS-UCB-Q algorithm can have a sub-linear growth over communication rounds under
certain conditions. Besides, the convergence performance of FL training is also
analyzed. Finally, simulation results validate the efficiency of the proposed
algorithms.
