Federated Learning Under Intermittent Client Availability and Time-Varying Communication Constraints. (arXiv:2205.06730v1 [cs.LG])

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 …

arxiv client communication constraints federated learning intermittent learning time