no code implementations • 29 May 2024 • Saber Malekmohammadi, Afaf Taik, Golnoosh Farnadi
Our proposed approach also addresses the server's uncertainties in clustering clients' model updates by employing larger batch sizes along with Gaussian Mixture Model (GMM) to alleviate the impact of noise and potential clustering errors, especially in privacy-sensitive scenarios.
no code implementations • 20 Mar 2024 • Khaoula Chehbouni, Megha Roshan, Emmanuel Ma, Futian Andrew Wei, Afaf Taik, Jackie CK Cheung, Golnoosh Farnadi
Despite growing mitigation efforts to develop safety safeguards, such as supervised safety-oriented fine-tuning and leveraging safe reinforcement learning from human feedback, multiple concerns regarding the safety and ingrained biases in these models remain.
no code implementations • 11 Jun 2023 • Maryam Molamohammadi, Afaf Taik, Nicolas Le Roux, Golnoosh Farnadi
The growing utilization of machine learning (ML) in decision-making processes raises questions about its benefits to society.
no code implementations • 7 Apr 2021 • Afaf Taik, Boubakr Nour, Soumaya Cherkaoui
In addition to preserving prosumers' privacy, we show through evaluations that training prediction models using Federated Learning yields high accuracy for different energy resources while reducing the communication overhead.
no code implementations • 18 Feb 2021 • Afaf Taik, Zoubeir Mlika, Soumaya Cherkaoui
As the data is the key component of the learning, we propose a new set of considerations for data characteristics in wireless scheduling algorithms in FEEL.