Summary of Dual-delayed Asynchronous Sgd For Arbitrarily Heterogeneous Data, by Xiaolu Wang et al.
Dual-Delayed Asynchronous SGD for Arbitrarily Heterogeneous Data
by Xiaolu Wang, Yuchang Sun, Hoi-To Wai, Jun Zhang
First submitted to arxiv on: 27 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed DuDe-ASGD algorithm addresses the limitations of traditional asynchronous stochastic gradient descent (SGD) in distributed learning scenarios where data is dispersed across multiple workers. Asynchronous SGD has been widely used to reduce synchronization overhead, but its performance often depends on a bounded dissimilarity condition among worker data. To overcome this limitation, the DuDe-ASGD algorithm makes full use of stale gradients from all workers during training, introducing two time lags in model parameters and data samples utilized by the server. This approach maintains a per-iteration computational cost comparable to traditional asynchronous SGD while achieving near-minimax-optimal convergence rates for smooth nonconvex problems. Numerical experiments demonstrate the effectiveness of DuDe-ASGD compared to existing algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces an algorithm called DuDe-ASGD that helps computers learn together when they have different pieces of information. This is important because sometimes, when we want many computers to work together, some of them might have very different things to share. The new algorithm, DuDe-ASGD, makes sure all the computers contribute equally and don’t get stuck because of their differences. It’s like a team working together to solve a problem! |
Keywords
» Artificial intelligence » Stochastic gradient descent
