1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.DC2023
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…
cs.LG2023★ 1 cited
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…