2 citations · 5 across the 11 of their papers we have counts for
4 papers · 1 filter
FedSpaLLM: Federated Pruning of Large Language Models
Guangji Bai, Yijiang Li, Zilinghan Li +2
Large Language Models (LLMs) achieve state-of-the-art performance but are challenging to deploy due to their high computational and storage demands. Pruning can reduce model size,…
Advances in APPFL: A Comprehensive and Extensible Federated Learning Framework
Zilinghan Li, Shilan He, Ze Yang +3
Federated learning (FL) is a distributed machine learning paradigm enabling collaborative model training while preserving data privacy. In today's landscape, where most data is pro…
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…