WAFA Bouras

nech abdennour , fateh zerouti

secureguard a software for encrypt multimedia files and steganography

abstract

Citation

HADJI Ayat , Belgacem Brahimi , BILAL Lounnas , WAFA Bouras , ,(2025-12-03), bloom taxonomy question classification across languages opportunities and challenges in arabic NLP,National conference on artificial intelligence - techniques and recent application,University of ziane achour djelfa

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.

Citation

WAFA Bouras , Mohamed Benouis , BRAHIM Bouderah , Samir Akhrouf , kameleddine.heraguemi@ensia.edu.dz, , (2025-06-16), Optimizing Security and Performance in Blockchain-Enhanced Federated Learning Through Participant Selection with Role Determination, Computing and Informatics, Vol:44, Issue:3, pages:35, OJS/PKP

To the best of current knowledge, the performance of federated learning predominantly depends on the efficiency of the aggregation server scheme utilized to consolidate model parameters received from distributed local devices. However, in practical scenarios, the global server often faces single-point failures due to four major issues: 1) variations in data distribution settings, such as independent identical distribution (IID) or non-independent identical distribution; 2) communication overhead; 3) limitations in hardware and resource storage availability; and 4) diverse participant participation behaviors. To address the latter concern, limited research has endeavored to establish a correlation between these heterogeneous settings and federated learning performance by analyzing different aspects of participant behavior. Inspired by the absence of a definitive verdict regarding the relationship between the global server and participant behavior, this paper investigates the aspect of participant selection methods and conducts a detailed comparative study among various participant selection methods

Citation

WAFA Bouras , Mohamed Benouis , Samir Akhrouf , brahim.bouderah@univ-msila.dz, ,(2024-05-13), Analysis Study of Participant Selection Methods in Federated Learning,ICEEAC’2024,Setif university