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Adaptive Stochastic Gradient Descent for Fast and Communication-Efficient Distributed Learning
Moreover, the results also show that the adaptive version is communication-efficient, where the amount of communication required between the master and the workers is less than that of non-adaptive versions.
Modeling Performance and Energy trade-offs in Online Data-Intensive Applications
The performance metrics under consideration are the mean number of applications in the system and the average energy expenditure.
Adaptive Distributed Stochastic Gradient Descent for Minimizing Delay in the Presence of Stragglers
One solution studied in the literature is to wait at each iteration for the responses of the fastest $k<n$ workers before updating the model, where $k$ is a fixed parameter.
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