3 citations · 3 across the 3 of their papers we have counts for
6 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…
Does Combining Parameter-efficient Modules Improve Few-shot Transfer Accuracy?
Nader Asadi, Mahdi Beitollahi, Yasser Khalil +3
Parameter-efficient fine-tuning stands as the standard for efficiently fine-tuning large language and vision models on downstream tasks. Specifically, the efficiency of low-rank ad…
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…
Calibrated One Round Federated Learning with Bayesian Inference in the Predictive Space
Mohsin Hasan, Guojun Zhang, Kaiyang Guo +2
Federated Learning (FL) involves training a model over a dataset distributed among clients, with the constraint that each client's dataset is localized and possibly heterogeneous.…
Cross Domain Generative Augmentation: Domain Generalization with Latent Diffusion Models
Sobhan Hemati, Mahdi Beitollahi, Amir Hossein Estiri +3
Despite the huge effort in developing novel regularizers for Domain Generalization (DG), adding simple data augmentation to the vanilla ERM which is a practical implementation of t…