Blockchain-Enabled Defense Mechanism for ...
Document type :
Communication dans un congrès avec actes
Permalink :
Title :
Blockchain-Enabled Defense Mechanism for Protecting Federated Learning Systems Against Malicious Node Updates
Author(s) :
Attiaoui, Adil [Auteur]
Ecole Nationale Supérieure d'Informatique et d'Analyses des Systèmes [ENSIAS]
Kobbane, Abdellatif [Auteur]
Ecole Nationale Supérieure d'Informatique et d'Analyses des Systèmes [ENSIAS]
Elhachmi, Jamal [Auteur]
Ecole Nationale Supérieure d'Informatique et d'Analyses des Systèmes [ENSIAS]
Ayaida, Marwane [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
COMmunications NUMériques - IEMN [COMNUM - IEMN]
Chougdali, Khalid [Auteur]
Université Ibn Tofaïl [UIT]
Ecole Nationale Supérieure d'Informatique et d'Analyses des Systèmes [ENSIAS]
Kobbane, Abdellatif [Auteur]
Ecole Nationale Supérieure d'Informatique et d'Analyses des Systèmes [ENSIAS]
Elhachmi, Jamal [Auteur]
Ecole Nationale Supérieure d'Informatique et d'Analyses des Systèmes [ENSIAS]
Ayaida, Marwane [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
COMmunications NUMériques - IEMN [COMNUM - IEMN]
Chougdali, Khalid [Auteur]
Université Ibn Tofaïl [UIT]
Conference title :
2024 4th Interdisciplinary Conference on Electrics and Computer (INTCEC)
City :
Chicago
Country :
Etats-Unis d'Amérique
Start date of the conference :
2024-06-11
Publisher :
IEEE
HAL domain(s) :
Physique [physics]
Sciences de l'ingénieur [physics]
Sciences de l'ingénieur [physics]
English abstract : [en]
This paper aims to investigate the convergence of Federated Learning (FL) and Blockchain technology to enhance the security and robustness of machine learning systems. As the utilization of mobile devices grows, managing ...
Show more >This paper aims to investigate the convergence of Federated Learning (FL) and Blockchain technology to enhance the security and robustness of machine learning systems. As the utilization of mobile devices grows, managing the complexities of unbalanced and non-independent data becomes imperative. Fed-erated Learning introduces a decentralized training approach, while Blockchain provides transparency and immutability. This study proposes a resilient solution for safeguarding FL against single points of failure (SPoF) and poisoning attacks, integrating a decentralized validation mechanism and a Proof-of-Stake consen-sus protocol. Extensive experimentation is conducted to assess the effectiveness of the proposed framework across varying degrees of adversarial device engagement. The results demonstrate the solution's efficacy in fortifying FL systems against malicious participants, with an impact observed up to 50% adversarial involvement. This underscores the potential of the approach to elevate security in the realm of decentralized machine learning.Show less >
Show more >This paper aims to investigate the convergence of Federated Learning (FL) and Blockchain technology to enhance the security and robustness of machine learning systems. As the utilization of mobile devices grows, managing the complexities of unbalanced and non-independent data becomes imperative. Fed-erated Learning introduces a decentralized training approach, while Blockchain provides transparency and immutability. This study proposes a resilient solution for safeguarding FL against single points of failure (SPoF) and poisoning attacks, integrating a decentralized validation mechanism and a Proof-of-Stake consen-sus protocol. Extensive experimentation is conducted to assess the effectiveness of the proposed framework across varying degrees of adversarial device engagement. The results demonstrate the solution's efficacy in fortifying FL systems against malicious participants, with an impact observed up to 50% adversarial involvement. This underscores the potential of the approach to elevate security in the realm of decentralized machine learning.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
Source :
Submission date :
2024-08-27T02:18:49Z