Optimizing Security and Performance in Blockchain-Enhanced Federated Learning Through Participant Selection with Role Determination

Authors

  • Wafa Bouras Computer Science, University of M’sila, PO Box 166 Ichebilia, M’sila, 28000, Algeria & LIM Laboratory, Souk Ahras University, Souk Ahras, Algeria
  • Kameleddine Heraguemi Computer Science, University of M’sila, PO Box 166 Ichebilia, M’sila, 28000, Algeria & National School of Artificial Intelligence, Sidi Abd Allah, Algiers, Algeria
  • Mohamed Benouis Computer Science, University of M’sila, PO Box 166 Ichebilia, M’sila, 28000, Algeria & Human-Centered Artificial Intelligence, University of Augsburg, 286159 Augsburg, Germany
  • Brahim Bouderah Computer Science, University of M’sila, PO Box 166 Ichebilia, M’sila, 28000, Algeria & University Abdelhamid Ibn Badis-Mostaganem, Mostaganem, Algeria
  • Samir Akhrouf Computer Science and Its Applications Laboratory of M’sila (LIAM), University of M’sila, PO Box 166 Ichebilia, M’sila, 28000, Algeria

Keywords:

Federated learning, participant selection, blockchain, malicious attacks, distributed systems

Abstract

Federated learning (FL) allows distributed devices to jointly train a global model while safeguarding the privacy of their local data. However, selecting and securing clients, especially in environments with potentially malicious participants, remains a critical challenge. This study proposes an innovative participant selection method to enhance both security and efficiency in centralized and decentralized FL frameworks. In the centralized framework, this method effectively excludes clients with weak privacy protections and optimization capabilities, thus increasing overall system security. For decentralized FL, a blockchain-supported approach is introduced, which further strengthens the robustness of the system. Using a dynamic role assignment algorithm, roles such as worker, validator, and miner are allocated based on security and performance metrics for each training round. The findings show that this method performs on a par with the scenarios free of malicious clients, demonstrating the value of blockchain technology in improving FL protocols. By addressing security vulnerabilities and improving training efficiency, this research contributes to the development of more secure and efficient FL systems, underscoring the importance of advanced participant selection and role assignment strategies.

Downloads

Download data is not yet available.

Published

2025-06-30

How to Cite

Bouras, W., Heraguemi, K., Benouis, M., Bouderah, B., & Akhrouf, S. (2025). Optimizing Security and Performance in Blockchain-Enhanced Federated Learning Through Participant Selection with Role Determination. Computing and Informatics, 44(3). Retrieved from http://147.213.75.17/ojs/index.php/cai/article/view/7452