  Gradient-based distributed learning in Parameter Server (PS) computing
architectures is subject to random delays due to straggling worker nodes, as
well as to possible communication bottlenecks between PS and workers. Solutions
have been recently proposed to separately address these impairments based on
the ideas of gradient coding, worker grouping, and adaptive worker selection.
This paper provides a unified analysis of these techniques in terms of
wall-clock time, communication, and computation complexity measures.
Furthermore, in order to combine the benefits of gradient coding and grouping
in terms of robustness to stragglers with the communication and computation
load gains of adaptive selection, novel strategies, named Lazily Aggregated
Gradient Coding (LAGC) and Grouped-LAG (G-LAG), are introduced. Analysis and
results show that G-LAG provides the best wall-clock time and communication
performance, while maintaining a low computational cost, for two representative
distributions of the computing times of the worker nodes.
