Straggler-Aware Distributed Learning: Communication–Computation Latency Trade-Off
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DOI: 10.3390/e22050544
Abstract: When gradient descent (GD) is scaled to many parallel workers for large-scale machine learning applications, its per-iteration computation time is limited by straggling workers. Straggling workers can be tolerated by assigning redundant computations and/or coding… read more here.
Keywords: latency; per iteration; learning; computation ... See more keywords