Name
Abstract | ||
|---|---|---|
The computational efficiency of the asynchronous stochastic gradient descent (ASGD) against its synchronous version has been well documented in recent works. Unfortunately, it usually works only for the situation that all workers retrieve data from a shared dataset. As data get larger and more distributed, new ideas are urgently needed to maintain the efficiency of ASGD for decentralized training.... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TCNS.2021.3059848 | IEEE Transactions on Control of Network Systems |
Keywords | DocType | Volume |
Training,Convergence,Machine learning algorithms,Computational modeling,Stochastic processes,Computer architecture,Delays | Journal | 8 |
Issue | ISSN | Citations |
3 | 2325-5870 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |