  We propose ResIST, a novel distributed training protocol for Residual
Networks (ResNets). ResIST randomly decomposes a global ResNet into several
shallow sub-ResNets that are trained independently in a distributed manner for
several local iterations, before having their updates synchronized and
aggregated into the global model. In the next round, new sub-ResNets are
randomly generated and the process repeats until convergence. By construction,
per iteration, ResIST communicates only a small portion of network parameters
to each machine and never uses the full model during training. Thus, ResIST
reduces the per-iteration communication, memory, and time requirements of
ResNet training to only a fraction of the requirements of full-model training.
In comparison to common protocols, like data-parallel training and
data-parallel training with local SGD, ResIST yields a decrease in
communication and compute requirements, while being competitive with respect to
model performance.
