  Federated Learning (FL) is a collaborative machine learning (ML) framework
that combines on-device training and server-based aggregation to train a common
ML model among distributed agents. In this work, we propose an asynchronous FL
design with periodic aggregation to tackle the straggler issue in FL systems.
Considering limited wireless communication resources, we investigate the effect
of different scheduling policies and aggregation designs on the convergence
performance. Driven by the importance of reducing the bias and variance of the
aggregated model updates, we propose a scheduling policy that jointly considers
the channel quality and training data representation of user devices. The
effectiveness of our channel-aware data-importance-based scheduling policy,
compared with state-of-the-art methods proposed for synchronous FL, is
validated through simulations. Moreover, we show that an ``age-aware''
aggregation weighting design can significantly improve the learning performance
in an asynchronous FL setting.
