7 citations · 20 across the 9 of their papers we have counts for
9 papers
Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data
Abhijit Chunduru, Majid Morafah, Mahdi Morafah +2
The inevitable presence of data heterogeneity has made federated learning very challenging. There are numerous methods to deal with this issue, such as local regularization, better…
Federated Learning Client Pruning for Noisy Labels
Mahdi Morafah, Hojin Chang, Chen Chen +1
Federated Learning (FL) enables collaborative model training across decentralized edge devices while preserving data privacy. However, existing FL methods often assume clean annota…
Towards Diverse Device Heterogeneous Federated Learning via Task Arithmetic Knowledge Integration
Mahdi Morafah, Vyacheslav Kungurtsev, Hojin Chang +2
Federated Learning has emerged as a promising paradigm for collaborative machine learning, while preserving user data privacy. Despite its potential, standard FL lacks support for…
Stable Diffusion-based Data Augmentation for Federated Learning with Non-IID Data
Mahdi Morafah, Matthias Reisser, Bill Lin +1
The proliferation of edge devices has brought Federated Learning (FL) to the forefront as a promising paradigm for decentralized and collaborative model training while preserving t…
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…
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…
Decentralized Asynchronous Non-convex Stochastic Optimization on Directed Graphs
Vyacheslav Kungurtsev, Mahdi Morafah, Tara Javidi +1
Distributed Optimization is an increasingly important subject area with the rise of multi-agent control and optimization. We consider a decentralized stochastic optimization proble…