5 citations · 15 across the 7 of their papers we have counts for
7 papers · 1 filter
A Practical Recipe for Federated Learning Under Statistical Heterogeneity Experimental Design
Mahdi Morafah, Weijia Wang, Bill Lin
Federated Learning (FL) has been an area of active research in recent years. There have been numerous studies in FL to make it more successful in the presence of data heterogeneity…
When Do Curricula Work in Federated Learning?
Saeed Vahidian, Sreevatsank Kadaveru, Woonjoon Baek +5
An oft-cited open problem of federated learning is the existence of data heterogeneity at the clients. One pathway to understanding the drastic accuracy drop in federated learning…
Neural Routing in Meta Learning
Jicang Cai, Saeed Vahidian, Weijia Wang +2
Meta-learning often referred to as learning-to-learn is a promising notion raised to mimic human learning by exploiting the knowledge of prior tasks but being able to adapt quickly…
Efficient Distribution Similarity Identification in Clustered Federated Learning via Principal Angles Between Client Data Subspaces
Saeed Vahidian, Mahdi Morafah, Weijia Wang +4
Clustered federated learning (FL) has been shown to produce promising results by grouping clients into clusters. This is especially effective in scenarios where separate groups of…
FLIS: Clustered Federated Learning via Inference Similarity for Non-IID Data Distribution
Mahdi Morafah, Saeed Vahidian, Weijia Wang +1
Classical federated learning approaches yield significant performance degradation in the presence of Non-IID data distributions of participants. When the distribution of each local…
Learning Accurate and Interpretable Decision Rule Sets from Neural Networks
Litao Qiao, Weijia Wang, Bill Lin
This paper proposes a new paradigm for learning a set of independent logical rules in disjunctive normal form as an interpretable model for classification. We consider the problem…
Unsupervised Meta-Learning through Latent-Space Interpolation in Generative Models
Siavash Khodadadeh, Sharare Zehtabian, Saeed Vahidian +3
Unsupervised meta-learning approaches rely on synthetic meta-tasks that are created using techniques such as random selection, clustering and/or augmentation. Unfortunately, cluste…