Search Results for author: Saeed Vahidian

Found 13 papers, 7 papers with code

Towards Building the Federated GPT: Federated Instruction Tuning

1 code implementation9 May 2023 Jianyi Zhang, Saeed Vahidian, Martin Kuo, Chunyuan Li, Ruiyi Zhang, Guoyin Wang, Yiran Chen

This repository offers a foundational framework for exploring federated fine-tuning of LLMs using heterogeneous instructions across diverse categories.

Federated Learning

When Do Curricula Work in Federated Learning?

no code implementations ICCV 2023 Saeed Vahidian, Sreevatsank Kadaveru, Woonjoon Baek, Weijia Wang, Vyacheslav Kungurtsev, Chen Chen, Mubarak Shah, Bill Lin

Specifically, we aim to investigate how ordered learning principles can contribute to alleviating the heterogeneity effects in FL.

Federated Learning

Neural Routing in Meta Learning

1 code implementation14 Oct 2022 Jicang Cai, Saeed Vahidian, Weijia Wang, Mohsen Joneidi, Bill Lin

Inspired by the widely recognized finding in neuroscience that distinct parts of the brain are highly specialized for different types of tasks, we aim to improve the model performance of the current meta learning algorithms by selectively using only parts of the model conditioned on the input tasks.


Rethinking Data Heterogeneity in Federated Learning: Introducing a New Notion and Standard Benchmarks

1 code implementation30 Sep 2022 Mahdi Morafah, Saeed Vahidian, Chen Chen, Mubarak Shah, Bill Lin

Though successful, federated learning presents new challenges for machine learning, especially when the issue of data heterogeneity, also known as Non-IID data, arises.

Federated Learning

Efficient Distribution Similarity Identification in Clustered Federated Learning via Principal Angles Between Client Data Subspaces

1 code implementation21 Sep 2022 Saeed Vahidian, Mahdi Morafah, Weijia Wang, Vyacheslav Kungurtsev, Chen Chen, Mubarak Shah, Bill Lin

This small set of principal vectors is provided to the server so that the server can directly identify distribution similarities among the clients to form clusters.

Federated Learning

FLIS: Clustered Federated Learning via Inference Similarity for Non-IID Data Distribution

1 code implementation20 Aug 2022 Mahdi Morafah, Saeed Vahidian, Weijia Wang, Bill Lin

Classical federated learning approaches yield significant performance degradation in the presence of Non-IID data distributions of participants.

Personalized Federated Learning

Personalized Federated Learning by Structured and Unstructured Pruning under Data Heterogeneity

1 code implementation2 May 2021 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.

Personalized Federated Learning

Asymptotic Optimality of Self-Representative Low-Rank Approximation and Its Applications

no code implementations1 Jan 2021 Saeed Vahidian, Mohsen Joneidi, Ashkan Esmaeili, Siavash Khodadadeh, Sharare Zehtabian, Ladislau Boloni, Nazanin Rahnavard, Bill Lin, Mubarak Shah

The approach is based on the concept of {\em self-rank}, defined as the minimum number of samples needed to reconstruct all samples with an accuracy proportional to the rank-$K$ approximation.

Coresets for Estimating Means and Mean Square Error with Limited Greedy Samples

no code implementations3 Jun 2019 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.

Clustering Node Classification

A Novel Low-Complexity Framework in Ultra-Wideband Imaging for Breast Cancer Detection

no code implementations8 Sep 2017 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 complexity.

Breast Cancer Detection

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