3 papers
cs.LG2026
Toward Enhancing Representation Learning in Federated Multi-Task Settings
Mehdi Setayesh, Mahdi Beitollahi, Yasser H. Khalil +1
Federated multi-task learning (FMTL) seeks to collaboratively train customized models for users with different tasks while preserving data privacy. Most existing approaches assume…
cs.RO2025
Two-Steps Diffusion Policy for Robotic Manipulation via Genetic Denoising
Mateo Clemente, Leo Brunswic, Rui Heng Yang +5
Diffusion models, such as diffusion policy, have achieved state-of-the-art results in robotic manipulation by imitating expert demonstrations. While diffusion models were originall…
cs.LG2025
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…