Refine and Identify: An Accelerated Iterative Algorithm for Securing Federated Learning
Date
2024Author
Gouissem, A.Chkirbene, Z.
Khattab, T.
Mabrok, M.
Abdallah, M.
Hamila, R.
...show more authors ...show less authors
Metadata
Show full item recordAbstract
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.
Collections
- Electrical Engineering [2611 items ]
- Mathematics, Statistics & Physics [722 items ]
Related items
Showing items related by title, author, creator and subject.
-
Machine Learning for Healthcare Wearable Devices: The Big Picture
Sabry, Farida; Eltaras, Tamer; Labda, Wadha; Alzoubi, Khawla; Malluhi, Qutaibah ( John Wiley and Sons Inc , 2022 , Article Review)Using artificial intelligence and machine learning techniques in healthcare applications has been actively researched over the last few years. It holds promising opportunities as it is used to track human activities and ... -
A cooperative Q-learning approach for online power allocation in femtocell networks
Saad H.; Mohamed A.; Elbatt T. ( IEEE , 2013 , Conference Paper)In this paper, we address the problem of distributed interference management of cognitive femtocells that share the same frequency range with macrocells using distributed multiagent Q-learning. We formulate and solve three ... -
A cooperative Q-learning approach for distributed resource allocation in multi-user femtocell networks
Saad H.; Mohamed A.; El Batt T. ( Institute of Electrical and Electronics Engineers Inc. , 2016 , Conference Paper)This paper studies distributed interference management for femtocells that share the same frequency band with macrocells. We propose a multi-agent learning technique based on distributed Q-learning, called subcarrier-based ...