24 citations · 28 across the 12 of their papers we have counts for
12 papers
FedACT: Concurrent Federated Intelligence across Heterogeneous Data Sources
Md Sirajul Islam, Isabelle G Chapman, N I Md Ashafuddula +4
Federated Learning (FL) enables collaborative intelligence across decentralized data source devices in a privacy-preserving way. While substantial research attention has been drawn…
M3R: Localized Rainfall Nowcasting with Meteorology-Informed MultiModal Attention
Sanjeev Panta, Rhett M Morvant, Xu Yuan +2
Accurate and timely rainfall nowcasting is crucial for disaster mitigation and water resource management. Despite recent advances in deep learning, precipitation prediction remains…
Revisiting the Seasonal Trend Decomposition for Enhanced Time Series Forecasting
Sanjeev Panta, Xu Yuan, Li Chen +1
Time series forecasting presents significant challenges in real-world applications across various domains. Building upon the decomposition of the time series, we enhance the archit…
Resource Heterogeneity-Aware and Utilization-Enhanced Scheduling for Deep Learning Clusters
Abeda Sultana, Nabin Pakka, Fei Xu +3
Scheduling deep learning (DL) models to train on powerful clusters with accelerators like GPUs and TPUs, presently falls short, either lacking fine-grained heterogeneity awareness…
SEAFL: Enhancing Efficiency in Semi-Asynchronous Federated Learning through Adaptive Aggregation and Selective Training
Md Sirajul Islam, Sanjeev Panta, Fei Xu +3
Federated Learning (FL) is a promising distributed machine learning framework that allows collaborative learning of a global model across decentralized devices without uploading th…
Incentive-Compatible Federated Learning with Stackelberg Game Modeling
Simin Javaherian, Bryce Turney, Li Chen +1
Federated Learning (FL) has gained prominence as a decentralized machine learning paradigm, allowing clients to collaboratively train a global model while preserving data privacy.…
GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models
Jiadong Lou, Xu Yuan, Rui Zhang +3
Graph neural networks (GNNs) have exhibited superior performance in various classification tasks on graph-structured data. However, they encounter the potential vulnerability from…
Regional Weather Variable Predictions by Machine Learning with Near-Surface Observational and Atmospheric Numerical Data
Yihe Zhang, Bryce Turney, Purushottam Sigdel +11
Accurate and timely regional weather prediction is vital for sectors dependent on weather-related decisions. Traditional prediction methods, based on atmospheric equations, often s…
Towards Robust Vision Transformer via Masked Adaptive Ensemble
Fudong Lin, Jiadong Lou, Xu Yuan +1
Adversarial training (AT) can help improve the robustness of Vision Transformers (ViT) against adversarial attacks by intentionally injecting adversarial examples into the training…
FedClust: Tackling Data Heterogeneity in Federated Learning through Weight-Driven Client Clustering
Md Sirajul Islam, Simin Javaherian, Fei Xu +3
Federated learning (FL) is an emerging distributed machine learning paradigm that enables collaborative training of machine learning models over decentralized devices without expos…
An Open and Large-Scale Dataset for Multi-Modal Climate Change-aware Crop Yield Predictions
Fudong Lin, Kaleb Guillot, Summer Crawford +3
Precise crop yield predictions are of national importance for ensuring food security and sustainable agricultural practices. While AI-for-science approaches have exhibited promisin…
FedClust: Optimizing Federated Learning on Non-IID Data through Weight-Driven Client Clustering
Md Sirajul Islam, Simin Javaherian, Fei Xu +3
Federated learning (FL) is an emerging distributed machine learning paradigm enabling collaborative model training on decentralized devices without exposing their local data. A key…