5 citations · 10 across the 6 of their papers we have counts for
7 papers · 1 filter
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…
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…