A federated learning-enabled predictive analysis to forecast stock market trends

Saeid Pourroostaei Ardakani, Nanjiang Du, Chenhong Lin, Jiun Chi Yang, Zhuoran Bi, Lejun Chen

Research output: Journal PublicationArticlepeer-review

7 Citations (Scopus)


This article proposes a federated learning framework to build Random Forest, Support Vector Machine, and Linear Regression models for stock market prediction. The performance of the federated learning is compared against centralised and decentralised learning frameworks to figure out the best fitting approach for stock market prediction. According to the results, federated learning outperforms both centralised and decentralised frameworks in terms of Mean Square Error if Random Forest (MSE = 0.021) and Support Vector Machine techniques (MSE = 37.596) are used, while centralised learning (MSE = 0.011) outperforms federated and decentralised frameworks if a linear regression model is used. Moreover, federated learning gives a better model training delay as compared to the benchmarks if Linear Regression (time = 9.7 s) and Random Forest models (time = 515 s) are used, whereas decentralised learning gives a minimised model training delay (time = 3847 s) for Support Vector Machine.

Original languageEnglish
Pages (from-to)4529-4535
Number of pages7
JournalJournal of Ambient Intelligence and Humanized Computing
Issue number4
Publication statusPublished - Apr 2023


  • Centralised learning
  • Decentralised learning
  • Federated learning
  • Stock market trend prediction

ASJC Scopus subject areas

  • General Computer Science


Dive into the research topics of 'A federated learning-enabled predictive analysis to forecast stock market trends'. Together they form a unique fingerprint.

Cite this