2 citations · 7 across the 15 of their papers we have counts for
3 papers · 1 filter
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…
CADRE: Customizable Assurance of Data Readiness in Privacy-Preserving Federated Learning
Kaveen Hiniduma, Zilinghan Li, Aditya Sinha +2
Privacy-Preserving Federated Learning (PPFL) is a decentralized machine learning approach where multiple clients train a model collaboratively. PPFL preserves the privacy and secur…
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…