  Wall-clock convergence time and communication rounds are critical performance
metrics in distributed learning with parameter-server setting. While
synchronous methods converge fast but are not robust to stragglers; and
asynchronous ones can reduce the wall-clock time per round but suffers from
degraded convergence rate due to the staleness of gradients, it is natural to
combine the two methods to achieve a balance. In this work, we develop a novel
asynchronous strategy that leverages the advantages of both synchronous methods
and asynchronous ones, named adaptive bounded staleness (ABS). The key enablers
of ABS are two-fold. First, the number of workers that the PS waits for per
round for gradient aggregation is adaptively selected to strike a
straggling-staleness balance. Second, the workers with relatively high
staleness are required to start a new round of computation to alleviate the
negative effect of staleness. Simulation results are provided to demonstrate
the superiority of ABS over state-of-the-art schemes in terms of wall-clock
time and communication rounds.
