92 citations · 425 across the 20 of their papers we have counts for
21 papers
Secure Federated Learning Across Heterogeneous Cloud and High-Performance Computing Resources -- A Case Study on Federated Fine-tuning of LLaMA 2
Zilinghan Li, Shilan He, Pranshu Chaturvedi +4
Federated learning enables multiple data owners to collaboratively train robust machine learning models without transferring large or sensitive local datasets by only sharing the p…
Self-Supervised Masked Digital Elevation Models Encoding for Low-Resource Downstream Tasks
Priyam Mazumdar, Aiman Soliman, Volodymyr Kindratenko +2
The lack of quality labeled data is one of the main bottlenecks for training Deep Learning models. As the task increases in complexity, there is a higher penalty for overfitting an…
FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices using a Computing Power Aware Scheduler
Zilinghan Li, Pranshu Chaturvedi, Shilan He +6
Cross-silo federated learning offers a promising solution to collaboratively train robust and generalized AI models without compromising the privacy of local datasets, e.g., health…
APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a Service
Zilinghan Li, Shilan He, Pranshu Chaturvedi +9
Cross-silo privacy-preserving federated learning (PPFL) is a powerful tool to collaboratively train robust and generalized machine learning (ML) models without sharing sensitive (e…
Detection, Instance Segmentation, and Classification for Astronomical Surveys with Deep Learning (DeepDISC): Detectron2 Implementation and Demonstration with Hyper Suprime-Cam Data
G. M. Merz, Y. Liu, C. J. Burke +4
The next generation of wide-field deep astronomical surveys will deliver unprecedented amounts of images through the 2020s and beyond. As both the sensitivity and depth of observat…
One-shot Generative Distribution Matching for Augmented RF-based UAV Identification
Amir Kazemi, Salar Basiri, Volodymyr Kindratenko +1
This work addresses the challenge of identifying Unmanned Aerial Vehicles (UAV) using radiofrequency (RF) fingerprinting in limited RF environments. The complexity and variability…