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Federated learning (FL) is typically performed in a synchronous parallelmanner, where the involvement of a slow client delays a training iteration.Current FL systems employ a participant selection strategy to select fastclients with quality data in each iteration. However, this is not alwayspossible in practice, and the selection strategy often has to navigate anunpleasant trade-off between the speed and the data quality of clients.  In this paper, we present Pisces, an asynchronous FL system with intelligentparticipant selection and model aggregation for accelerated training. To avoidincurring excessive resource cost and stale training computation, Pisces uses anovel scoring mechanism to identify suitable clients to participate in atraining iteration. It also adapts the pace of model aggregation to dynamicallybound the progress gap between the selected clients and the server, with aprovable convergence guarantee in a smooth non-convex setting. We haveimplemented Pisces in an open-source FL platform called Plato, and evaluatedits performance in large-scale experiments with popular vision and languagemodels. Pisces outperforms the state-of-the-art synchronous and asynchronousschemes, accelerating the time-to-accuracy by up to 2.0x and 1.9x,respectively.

Pisces: Efficient Federated Learning via Guided Asynchronous Training