2 citations · 5 across the 14 of their papers we have counts for
5 papers · 1 filter
Provably Communication-Efficient and Privacy-Preserving Federated Graph Neural Networks
Zhishuai Guo, Wenhan Wu, Chen Chen +3
Graph neural networks (GNNs) achieve strong performance on relational data, but real-world graphs are often distributed across organizations that cannot share raw data due to priva…
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…
APPFL: Open-Source Software Framework for Privacy-Preserving Federated Learning
Minseok Ryu, Youngdae Kim, Kibaek Kim +1
Federated learning (FL) enables training models at different sites and updating the weights from the training instead of transferring data to a central location and training as in…