Asynchronous decentralized accelerated stochastic gradient descent
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Abstract (arXiv)
In this work, we introduce an asynchronous decentralized accelerated stochastic gradient descent type of method for decentralized stochastic optimization, considering communication and synchronization are the major bottlenecks. We establish O(1/ε) (resp., O(1/√ε)) communication complexity and O(1/ε2) (resp., O(1/ε)) sampling complexity for solving general convex (resp., strongly convex) problems.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Sep 2018.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 1.
2 papers.
- 2021Truly Sparse Neural Networks at Scalecited by 15
- 2023An Asynchronous Decentralized Algorithm for Wasserstein Barycenter Problemcited by 0
- 2023An Asynchronous Decentralized Algorithm for Wasserstein Barycenter Problem23 Apr 2023
- 2021Truly Sparse Neural Networks at Scale2 Feb 2021