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Jun 14, 2019 · Abstract:Distributed optimization often consists of two updating phases: local optimization and inter-node communication.
Abstract. Decentralized optimization are playing an important role in applications such as training large machine learning models, among others. Despite its ...
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Distributed optimization often consists of two updating phases: local optimization and inter-node communication. Conventional approaches require working ...
Oct 31, 2022 · This paper considers the following problem in distributed optimization: To train an overparameterized model over a set of distributed nodes, ...
In this work, we focus on distributed optimization for 'large' machine learning models (i.e., overpa- rameterized problems, to be defined shortly), and we ask ...
We consider distributed optimization with degenerate loss functions, where the optimal sets of local loss functions have a non-empty intersection.
Optimization in distributed networks plays a central role in almost all distributed machine learning problems. In principle, the use of distributed task ...
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 ...
Decentralized optimization are playing an important role in applications such as training large machine learning models, among others.
communication complexities. 45. In this work, we focus on distributed optimization for 'large' machine learning models (i.e., overpa-. 46 rameterized problems ...