https://scholars.lib.ntu.edu.tw/handle/123456789/611210
Title: | Online Extreme Learning Machine Design for the Application of Federated Learning | Authors: | AN-YEU(ANDY) WU | Keywords: | Federated learning; online sequential extreme learning machine | Issue Date: | 2020 | Start page/Pages: | 188-192 | Source: | Proceedings - 2020 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2020 | Abstract: | In this paper, we propose a federated extreme learning machine system (Fed-ELMS) to meet the demand for federated learning scenarios. In the scenario of federated learning, data is kept on edge devices to preserve the privacy of data, while metadata, such as model parameters, are exchanged between a centralized cloud server and edge devices. Despite non-independent and identically distributed (non-IID) and imbalanced data across edge devices, we show that Fed-ELMS can still achieve comparable performance with only 3.3% accuracy loss compared to a centralized ELM trained with IID and balanced data. (a) Moreover, by quantizing input weights and biases, parameters of a model and transmission power consumption between cloud and edge are dramatically reduced. Compared With conventional neural networks (NNs) with the same transmission cost, the proposed Fed-ELMS outperforms FederatedAveraging NN (Fed- NN) by 2.3% accuracy and the fine-tuning process is 7%-33% less time-consuming. Therefore, the proposed Fed-ELMS is a promising system for edge devices to support the future trend of federated learning. © 2020 IEEE. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084986459&doi=10.1109%2fAICAS48895.2020.9073802&partnerID=40&md5=0d6ba8a8386b81c07402ab393049e312 https://scholars.lib.ntu.edu.tw/handle/123456789/611210 |
ISBN: | 9.78173E+12 | DOI: | 10.1109/AICAS48895.2020.9073802 | SDG/Keyword: | Data privacy; Green computing; Knowledge acquisition; Machine design; Machine learning; Extreme learning machine; Future trends; Imbalanced data; Learning scenarios; Model parameters; Neural networks (NNS); Transmission costs; Transmission power; E-learning |
Appears in Collections: | 電機工程學系 |
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