5 citations · 22 across the 11 of their papers we have counts for
11 papers
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…
Rethinking Data Heterogeneity in Federated Learning: Introducing a New Notion and Standard Benchmarks
Mahdi Morafah, Saeed Vahidian, Chen Chen +2
Though successful, federated learning presents new challenges for machine learning, especially when the issue of data heterogeneity, also known as Non-IID data, arises. To cope wit…
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…
LiteCON: An All-Photonic Neuromorphic Accelerator for Energy-efficient Deep Learning (Preprint)
Dharanidhar Dang, Bill Lin, Debashis Sahoo
Deep learning is highly pervasive in today's data-intensive era. In particular, convolutional neural networks (CNNs) are being widely adopted in a variety of fields for superior ac…
Personalized Federated Learning by Structured and Unstructured Pruning under Data Heterogeneity
Saeed Vahidian, Mahdi Morafah, Bill Lin
The traditional approach in FL tries to learn a single global model collaboratively with the help of many clients under the orchestration of a central server. However, learning a s…
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…
Differentially-private Federated Neural Architecture Search
Ishika Singh, Haoyi Zhou, Kunlin Yang +3
Neural architecture search, which aims to automatically search for architectures (e.g., convolution, max pooling) of neural networks that maximize validation performance, has achie…
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…