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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| Vydáno v: | Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) s. 3947 - 3951 |
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01.05.2020
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| ISSN: | 2379-190X |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Skoglund, Mikael Javid, Alireza M. Chatterjee, Saikat Liang, Xinyue |
| Author_xml | – sequence: 1 givenname: Xinyue surname: Liang fullname: Liang, Xinyue organization: KTH Royal Institute of Technology,Division of Information Science and Engineering School of Electrical Engineering and Computer Science,Sweden – sequence: 2 givenname: Alireza M. surname: Javid fullname: Javid, Alireza M. organization: KTH Royal Institute of Technology,Division of Information Science and Engineering School of Electrical Engineering and Computer Science,Sweden – sequence: 3 givenname: Mikael surname: Skoglund fullname: Skoglund, Mikael organization: KTH Royal Institute of Technology,Division of Information Science and Engineering School of Electrical Engineering and Computer Science,Sweden – sequence: 4 givenname: Saikat surname: Chatterjee fullname: Chatterjee, Saikat organization: KTH Royal Institute of Technology,Division of Information Science and Engineering School of Electrical Engineering and Computer Science,Sweden |
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| Snippet | In this work, we exploit an asynchronous computing framework namely ARock to learn a deep neural network called self-size estimating feedforward neural network... |
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| SubjectTerms | Asynchronous Communication networks convex optimization decentralized learning Feedforward neural networks neural networks Optimization Signal processing Signal processing algorithms Sparse matrices Speech processing |
| Title | Asynchrounous Decentralized Learning of a Neural Network |
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