Cost-Efficient Distributed Learning via Combinatorial Multi-Armed Bandits. (arXiv:2202.08302v2 [cs.IT] UPDATED)

We consider the distributed SGD problem, where a main node distributes
gradient calculations among $n$ workers. By assigning tasks to all the workers
and waiting only for the $k$ fastest ones, the main node can trade-off the
algorithm's error with its runtime by gradually increasing $k$ as the algorithm
evolves. However, this strategy, referred to as adaptive $k$-sync, neglects the
cost of unused computations and of communicating models to workers that reveal
a straggling behavior. We propose a cost-efficient scheme …

arxiv cost distributed learning multi-armed bandits