تصفح حسب الموضوع "Byzantine attacks"

السجلات المعروضة 1 -- 5 من 5

    • Collaborative Byzantine Resilient Federated Learning 

      Gouissem, A.; Abualsaud, K.; Yaacoub, E.; Khattab, T.; Guizani, M. ( Institute of Electrical and Electronics Engineers Inc. , 2023 , Article)
      Federated learning (FL) enables an effective and private distributed learning process. However, it is vulnerable against several types of attacks, such as Byzantine behaviors. The first purpose of this work is to demonstrate ...

    • Federated Learning Stability Under Byzantine Attacks 

      Gouissem, A.; Abualsaud, K.; Yaacoub, E.; Khattab, T.; Guizani, M. ( Institute of Electrical and Electronics Engineers Inc. , 2022 , Conference Paper)
      Federated Learning (FL) is a machine learning approach that enables private and decentralized model training. Although FL has been shown to be very useful in several applications, its privacy constraints cause a lack of ...

    • Federating Learning Attacks: Maximizing Damage while Evading Detection 

      Gouissem, A.; Khattab, T.; Abdalla,h M.; Mohamed, A. ( IEEE , 2023 , Conference Paper)
      Despite its potential benefits, Federated learning (FL) is vulnerable to various types of attacks that can compromise the accuracy and security of the trained model. While several defense mechanisms have been proposed to ...

    • Refine and Identify: An Accelerated Iterative Algorithm for Securing Federated Learning 

      Gouissem, A.; Chkirbene, Z.; Khattab, T.; Mabrok, M.; Abdallah, M.; ... more authors ( IEEE , 2024 , Conference Paper)
      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) ...

    • Robust Decentralized Federated Learning Using Collaborative Decisions 

      Gouissem, A.; Abualsaud, K.; Yaacoub, E.; Khattab, T.; Guizani, M. ( Institute of Electrical and Electronics Engineers Inc. , 2022 , Conference Paper)
      Federated Learning (FL) has attracted a lot of attention in numerous applications due to recent data privacy regulations and increased awareness about data handling issues, combined with the ever-increasing big-data sizes. ...