3 citations · 3 across the 1 of their papers we have counts for
3 papers
NoT: Federated Unlearning via Weight Negation
Yasser H. Khalil, Leo Brunswic, Soufiane Lamghari +3
Federated unlearning (FU) aims to remove a participant's data contributions from a trained federated learning (FL) model, ensuring privacy and regulatory compliance. Traditional FU…
Parametric Feature Transfer: One-shot Federated Learning with Foundation Models
Mahdi Beitollahi, Alex Bie, Sobhan Hemati +4
In one-shot federated learning (FL), clients collaboratively train a global model in a single round of communication. Existing approaches for one-shot FL enhance communication effi…
DFML: Decentralized Federated Mutual Learning
Yasser H. Khalil, Amir H. Estiri, Mahdi Beitollahi +5
In the realm of real-world devices, centralized servers in Federated Learning (FL) present challenges including communication bottlenecks and susceptibility to a single point of fa…