A Starfish Optimization Algorithm-Based Federated Learning Approach for Financial Risk Prediction in Manufacturing Enterprises

During digital transformation, manufacturing enterprises encounter challenges such as the high cost of smart devices, operational interruptions, and increased technology expenses, raising their financial risks. Addressing the digital transformation challenges confronting manufacturing enterprises necessitates developing an intelligent financial risk prediction system leveraging AI technologies like big data and deep learning, enabling enterprises to mitigate financial exposure. In addition, some data of some manufacturing enterprises cannot be disclosed and shared due to the involvement of trade secrets and shareholder interests. To address these challenges, this study proposes a federated learning (FL)-based framework for predicting financial risk in manufacturing enterprises. Without sharing data, each client (manufacturing enterprise) in the FL framework uses deep learning models to train financial risk prediction models through a central server federation. In this study, the proposed FL framework employs a deep learning model based on a neural Turing machine (NTM) with a long short-term memory (LSTM) controller. In addition, in order to improve the prediction accuracy of the hybrid NTM-FL model, an improved starfish optimization algorithm (ISFOA) was used to optimize the structure of the NTM model. Finally, the experimental results showed that the ISFOA-based NTM-FL (ISFOA-NTM-FL) model improved the prediction accuracy by 26.32% compared to the other three financial risk prediction models.

@article{Liu2025,
  title     = {A Starfish Optimization Algorithm-Based Federated Learning Approach for Financial Risk Prediction in Manufacturing Enterprises},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {9},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Bin Liu and Liang Chen and Haitong Jiang and Rui Ma},
  doi       = {10.14569/IJACSA.2025.0160918},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160918}
}