9 citations · 37 across the 13 of their papers we have counts for
14 papers
CEFHRI: A Communication Efficient Federated Learning Framework for Recognizing Industrial Human-Robot Interaction
Umar Khalid, Hasan Iqbal, Saeed Vahidian +2
Human-robot interaction (HRI) is a rapidly growing field that encompasses social and industrial applications. Machine learning plays a vital role in industrial HRI by enhancing the…
Towards Building the Federated GPT: Federated Instruction Tuning
Jianyi Zhang, Saeed Vahidian, Martin Kuo +6
While "instruction-tuned" generative large language models (LLMs) have demonstrated an impressive ability to generalize to new tasks, the training phases heavily rely on large amou…
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…
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…
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…
Coresets for Estimating Means and Mean Square Error with Limited Greedy Samples
Saeed Vahidian, Baharan Mirzasoleiman, Alexander Cloninger
In a number of situations, collecting a function value for every data point may be prohibitively expensive, and random sampling ignores any structure in the underlying data. We int…
On the Security Analysis of a Cooperative Incremental Relaying Protocol in the Presence of an Active Eavesdropper
Saeed Vahidian, Sajad Hatamnia, Benoit Champagne
Physical layer security offers an efficient means to decrease the risk of confidential information leakage through wiretap links. In this paper, we address the physical-layer secur…
A Novel Low-Complexity Framework in Ultra-Wideband Imaging for Breast Cancer Detection
Yasaman Ettefagh, Mohammad Hossein Moghaddam, Saeed Vahidian
In this research work, a novel framework is pro- posed as an efficient successor to traditional imaging methods for breast cancer detection in order to decrease the computational c…
A Framework for Super-Resolution of Scalable Video via Sparse Reconstruction of Residual Frames
Mohammad Hossein Moghaddam, Mohammad Javad Azizipour, Saeed Vahidian +1
This paper introduces a framework for super-resolution of scalable video based on compressive sensing and sparse representation of residual frames in reconnaissance and surveillanc…
Power Allocation and Cooperative Diversity in Two-Way Non-Regenerative Cognitive Radio Networks
Saeed Vahidian, Maryam Najafi, Marzieh Najafi +1
In this paper, we investigate the performance of a dual-hop block fading cognitive radio network with underlay spectrum sharing over independent but not necessarily identically dis…