  Federated learning systems facilitate training of global models in settings
where potentially heterogeneous data is distributed across a large number of
clients. Such systems operate in settings with intermittent client availability
and/or time-varying communication constraints. As a result, the global models
trained by federated learning systems may be biased towards clients with higher
availability. We propose F3AST, an unbiased algorithm that dynamically learns
an availability-dependent client selection strategy which asymptotically
minimizes the impact of client-sampling variance on the global model
convergence, enhancing performance of federated learning. The proposed
algorithm is tested in a variety of settings for intermittently available
clients under communication constraints, and its efficacy demonstrated on
synthetic data and realistically federated benchmarking experiments using
CIFAR100 and Shakespeare datasets. We show up to 186% and 8% accuracy
improvements over FedAvg, and 8% and 7% over FedAdam on CIFAR100 and
Shakespeare, respectively.
