Asynchrounous Decentralized Learning of a Neural Network

In this work, we exploit an asynchronous computing framework namely ARock to learn a deep neural network called self-size estimating feedforward neural network (SSFN) in a decentralized scenario. Using this algorithm namely asynchronous decentralized SSFN (dSSFN), we provide the centralized equivale...

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Veröffentlicht in:Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) S. 3947 - 3951
Hauptverfasser: Liang, Xinyue, Javid, Alireza M., Skoglund, Mikael, Chatterjee, Saikat
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.05.2020
Schlagworte:
Asynchronous
Communication networks
convex optimization
decentralized learning
Feedforward neural networks
neural networks
Optimization
Signal processing
Signal processing algorithms
Sparse matrices
Speech processing
ISSN:2379-190X
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Beschreibung
Zusammenfassung:In this work, we exploit an asynchronous computing framework namely ARock to learn a deep neural network called self-size estimating feedforward neural network (SSFN) in a decentralized scenario. Using this algorithm namely asynchronous decentralized SSFN (dSSFN), we provide the centralized equivalent solution under certain technical assumptions. Asynchronous dSSFN relaxes the communication bottleneck by allowing one node activation and one side communication, which reduces the communication overhead significantly, consequently increasing the learning speed. We compare asynchronous dSSFN with traditional synchronous dSSFN in the experimental results, which shows the competitive performance of asynchronous dSSFN, especially when the communication network is sparse.
ISSN:2379-190X
DOI:10.1109/ICASSP40776.2020.9053996