no code implementations • 30 Sep 2023 • Alexander Galozy, Sadi Alawadi, Victor Kebande, Sławomir Nowaczyk
This paper investigates the issue of privacy in a learning scenario where users share knowledge for a recommendation task.
no code implementations • 19 Sep 2023 • Sadi Alawadi, Addi Ait-Mlouk, Salman Toor, Andreas Hellander
In this paper, we propose and evaluate a FL strategy inspired by transfer learning in order to reduce resource utilization on devices, as well as the load on the server and network in each global training round.
no code implementations • 31 May 2023 • Sadi Alawadi, Khalid Alkharabsheh, Fahed Alkhabbas, Victor Kebande, Feras M. Awaysheh, Fabio Palomba, Mohammed Awad
This was followed by experiment 2, which was concerned with cross-evaluation, where each ML model was trained using one dataset, which was then evaluated over the other two datasets.
no code implementations • 4 Apr 2023 • Addi Ait-Mlouk, Sadi Alawadi, Salman Toor, Andreas Hellander
The POC combines Deep Bidirectional Transformer models and federated learning algorithms to protect customer data privacy during collaborative model training.
1 code implementation • 9 Feb 2022 • Addi Ait-Mlouk, Sadi Alawadi, Salman Toor, Andreas Hellander
In addition, we present the architecture and implementation of the system, as well as provide a reference evaluation based on the SQUAD dataset, to showcase how it overcomes data privacy issues and enables knowledge sharing between alliance members in a Federated learning setting.
2 code implementations • 27 Feb 2021 • Morgan Ekmefjord, Addi Ait-Mlouk, Sadi Alawadi, Mattias Åkesson, Prashant Singh, Ola Spjuth, Salman Toor, Andreas Hellander
Federated machine learning has great promise to overcome the input privacy challenge in machine learning.