Large language models, such as BERT, GPT-3, and T5, while powerful in identifying intricate patterns, pose privacy concerns due to the risk of exposing sensitive user information. A possible solution is machine unlearning, a method that allows for specific data elimination from trained models without the need for thorough retraining. Nevertheless, prevailing unlearning techniques designed…
Researchers from Sony AI and the King Abdullah University of Science and Technology (KAUST) have developed FedP3, a solution aimed at addressing the challenges of model heterogeneity in federated learning (FL). Model heterogeneity arises when devices used in FL have different capabilities and data distributions. FedP3, which stands for Federated Personalized and Privacy-friendly network Pruning,…