Refine and Identify: An Accelerated Iterative Algorithm for Securing Federated Learning
Author | Gouissem, A. |
Author | Chkirbene, Z. |
Author | Khattab, T. |
Author | Mabrok, M. |
Author | Abdallah, M. |
Author | Hamila, R. |
Available date | 2024-08-19T05:21:32Z |
Publication Date | 2024 |
Publication Name | 20th International Wireless Communications and Mobile Computing Conference, IWCMC 2024 |
Resource | Scopus |
Abstract | The identification of malicious users within a large set of participants poses a significant challenge in the domains of cybersecurity, data integrity, user management, and particularly within federated learning (FL) environments. FL, a distributed machine learning approach, necessitates rigorous mechanisms for safeguarding data integrity, model accuracy by effectively managing and identifying malicious participants. Traditional methods require the sequential removal and evaluation of users to determine their impact on the system's overall error rate or loss function, fall short in terms of efficiency and scalability, especially in FL contexts where data is distributed across multiple clients. To address these limitations, we propose the Refine and Identify Algorithm, a two-phased approach that efficiently narrows the search space for identifying malicious users by initially evaluating users in groups rather than individually and iteratively focusing on those groups with the highest potential for containing malicious users. A rigorous mathematical framework, including a proof of convergence and a detailed analysis of iteration necessities, underpins the algorithm's efficacy. The convergence proof and analysis of iteration requirements provide a solid mathematical foundation for the proposed method's effectiveness, paving the way for further optimization and application-specific tuning. Simulation results depict the efficiency of the proposed technique and show a significant reduction in computational resources and time required for identifying malicious users. |
Sponsor | This publication was made possible by the NPRP award NPRP13S-0201-200219 and by Qatar University Internal Grant IRCC-2020-001. The statements made herein are solely the responsibility of the authors. |
Language | en |
Publisher | IEEE |
Subject | Computational complexity Cybersecurity Iterative methods Learning systems Byzantine attacks Convergence analysis Cyber security Data integrity Distributed machine learning Federated learning Iterative algorithm Learning environments Security User management Efficiency |
Type | Conference Paper |
Pagination | 1767-1772 |
Files in this item
This item appears in the following Collection(s)
-
Electrical Engineering [2616 items ]
-
Mathematics, Statistics & Physics [726 items ]