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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.
Virtuoso: Video-based Intelligence for real-time tuning on SOCs
First, the system does not consider energy consumption of the models while making a decision on which model to run.
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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