Adaptive Distributed Stochastic Gradient Descent For Minimizing Delay In The Presence Of Stragglers
Serge Kas Hanna, Rawad Bitar, Salim El Rouayheb, Parimal Parag, Venkat Dasari
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04 May 2020
We consider the setting where a master wants to run a distributed stochastic gradient descent (SGD) algorithm on $n$ workers each having a subset of the data. Distributed SGD may suffer from the effect of stragglers, i.e., slow or unresponsive workers who cause delays. One solution studied in the literature is to wait at each iteration for the responses of the fastest $k
