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Jun 14, 2019 · Abstract:Distributed optimization often consists of two updating phases: local optimization and inter-node communication.
Distributed optimization often consists of two updating phases: local optimization and inter-node communication. Conventional approaches require working ...
In this work, we address the following open research question: To train an overparameterized model over a set of distributed nodes, what is the {\it minimum} ...
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Decentralized optimization are playing an important role in applications such as training large machine learning models, among others. Despite its superior.
Oct 31, 2022 · This paper considers the following problem in distributed optimization: To train an overparameterized model over a set of distributed nodes, ...
Jun 14, 2019 · It is shown that the more local updating can reduce the overall communication, even for an infinity number of steps where each node is free ...
Conventional approaches require working nodes to communicate with the server every one or few iterations to guarantee convergence. In this paper, we establish a ...
In this work, we address the following open research question: To train an overparameterized model over a set of distributed nodes, what is the minimum ...
We consider distributed optimization with degenerate loss functions, where the optimal sets of local loss functions have a non-empty intersection.
question: To train an overparameterized model over a set of distributed nodes, what ... Loss landscapes and optimization in over-parameterized non-linear. 384.