3 citations · 5 across the 7 of their papers we have counts for
3 papers · 1 filter
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…