<i>F-LSTM</i>: Federated learning-based LSTM framework for cryptocurrency price prediction

IF 16.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Nihar Patel, Nakul Vasani, Nilesh Kumar Jadav, Rajesh Gupta, Sudeep Tanwar, Zdzislaw Polkowski, Fayez Alqahtani, Amr Gafar
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引用次数: 0

Abstract

In this paper, a distributed machine-learning strategy, i.e., federated learning (FL), is used to enable the artificial intelligence (AI) model to be trained on dispersed data sources. The paper is specifically meant to forecast cryptocurrency prices, where a long short-term memory (LSTM)-based FL network is used. The proposed framework, i.e., F-LSTM utilizes FL, due to which different devices are trained on distributed databases that protect the user privacy. Sensitive data is protected by staying private and secure by sharing only model parameters (weights) with the central server. To assess the effectiveness of F-LSTM, we ran different empirical simulations. Our findings demonstrate that F-LSTM outperforms conventional approaches and machine learning techniques by achieving a loss minimal of $ 2.3 \times 10^{-4} $. Furthermore, the F-LSTM uses substantially less memory and roughly half the CPU compared to a solely centralized approach. In comparison to a centralized model, the F-LSTM requires significantly less time for training and computing. The use of both FL and LSTM networks is responsible for the higher performance of our suggested model (F-LSTM). In terms of data privacy and accuracy, F-LSTM addresses the shortcomings of conventional approaches and machine learning models, and it has the potential to transform the field of cryptocurrency price prediction.

<i>F-LSTM</i>:用于加密货币价格预测的基于联邦学习的LSTM框架
本文采用一种分布式机器学习策略,即联邦学习(FL),使人工智能(AI)模型能够在分散的数据源上进行训练。该论文专门用于预测加密货币价格,其中使用了基于长短期记忆(LSTM)的FL网络。建议的框架,即<italic>F-LSTM</italic>利用FL,因此不同的设备在保护用户隐私的分布式数据库上进行训练。通过仅与中央服务器共享模型参数(权重)来保持敏感数据的私密性和安全性,从而保护敏感数据。为了评估<italic>F-LSTM</italic>的有效性,我们进行了不同的经验模拟。我们的研究结果表明< italital>F-LSTM</italic>通过实现2.3 \乘以10^{-4}$的最小损失,优于传统方法和机器学习技术。此外,<italic>F-LSTM</italic>与完全集中的方法相比,使用的内存少得多,大约只有一半的CPU。与集中式模型相比,F-LSTM<需要更少的训练和计算时间。FL和LSTM网络的使用对我们建议的模型的更高性能负责(<italic>F-LSTM</italic>)。在数据隐私和准确性方面,< italital>F-LSTM</italic>解决了传统方法和机器学习模型的缺点,并有可能改变加密货币价格预测领域。</p></abstract>
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来源期刊
Accounts of Chemical Research
Accounts of Chemical Research 化学-化学综合
CiteScore
31.40
自引率
1.10%
发文量
312
审稿时长
2 months
期刊介绍: Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.
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