Search Results for author:
Found 5 papers, 1 papers with code
Does Combining Parameter-efficient Modules Improve Few-shot Transfer Accuracy?
Parameter-efficient fine-tuning stands as the standard for efficiently fine-tuning large language and vision models on downstream tasks.
DFML: Decentralized Federated Mutual Learning
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 failure.
Parametric Feature Transfer: One-shot Federated Learning with Foundation Models
This paper introduces FedPFT (Federated Learning with Parametric Feature Transfer), a methodology that harnesses the transferability of foundation models to enhance both accuracy and communication efficiency in one-shot FL.
Cross Domain Generative Augmentation: Domain Generalization with Latent Diffusion Models
The VRM reduces the estimation error in ERM by replacing the point-wise kernel estimates with a more precise estimation of true data distribution that reduces the gap between data points \textbf{within each domain}.
Understanding the Role of Layer Normalization in Label-Skewed Federated Learning
In this work, we reveal the profound connection between layer normalization and the label shift problem in federated learning.
