Browsing by Subject "Byzantine attacks"

Now showing items 1-3 of 3

    • 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 ...

    • 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. ...