2 citations · 5 across the 11 of their papers we have counts for
7 papers · 1 filter
Towards Global Federated Genome-Wide Association Meta-Analysis Using GA4GH TES
Abhijit Chunduru, Matthew Joel, Zilinghan Li +1
Genome-wide association studies (GWAS) gain statistical power from large, ancestrally diverse cohorts, but privacy regulations and data-residency constraints often prevent genomic…
FedQueue: Queue-Aware Federated Learning for Cross-Facility HPC Training
Yijiang Li, Emon Dey, Zilinghan Li +3
Federated learning (FL) across multiple HPC facilities faces stochastic admission delays from batch schedulers that dominate wall-clock time. Synchronous FL suffers from severe str…
Experiences Building Enterprise-Level Privacy-Preserving Federated Learning to Power AI for Science
Zilinghan Li, Aditya Sinha, Yijiang Li +3
Federated learning (FL) is a promising approach to enabling collaborative model training without centralized data sharing, a crucial requirement in scientific domains where data pr…
FedCostAware: Enabling Cost-Aware Federated Learning on the Cloud
Aditya Sinha, Zilinghan Li, Tingkai Liu +3
Federated learning (FL) is a distributed machine learning (ML) approach that allows multiple clients to collaboratively train ML models without exchanging original training data, o…
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…
Enabling End-to-End Secure Federated Learning in Biomedical Research on Heterogeneous Computing Environments with APPFLx
Trung-Hieu Hoang, Jordan Fuhrman, Ravi Madduri +8
Facilitating large-scale, cross-institutional collaboration in biomedical machine learning projects requires a trustworthy and resilient federated learning (FL) environment to ensu…