Distributed Stochastic Gradient Descent and Convergence to Local Minima

5 Mar 2020Brian SwensonRyan MurraySoummya KarH. Vincent Poor

In centralized settings, it is well known that stochastic gradient descent (SGD) avoids saddle points. However, similar guarantees are lacking for distributed first-order algorithms in nonconvex optimization.The paper studies distributed stochastic gradient descent (D-SGD)--a simple network-based implementation of SGD... (read more)

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