FedHiSyn: A hierarchical synchronous federated learning framework for resource and data heterogeneity

G Li, Y Hu, M Zhang, J Liu, Q Yin, Y Peng… - Proceedings of the 51st …, 2022 - dl.acm.org
Federated Learning (FL) enables training a global model without sharing the decentralized
raw data stored on multiple devices to protect data privacy. Due to the diverse capacity of the …
被引用次数：27 相关文章

FedHiSyn: A Hierarchical Synchronous Federated Learning Framework for Resource and Data Heterogeneity

G Li, Y Hu, M Zhang, J Liu, Q Yin, Y Peng… - arXiv preprint arXiv …, 2022 - arxiv.org
Federated Learning (FL) enables training a global model without sharing the decentralized
raw data stored on multiple devices to protect data privacy. Due to the diverse capacity of the …

FedHiSyn: A Hierarchical Synchronous Federated Learning Framework for Resource and Data Heterogeneity

G Li, Y Hu, M Zhang, J Liu, Q Yin, Y Peng… - arXiv e …, 2022 - ui.adsabs.harvard.edu
Federated Learning (FL) enables training a global model without sharing the decentralized
raw data stored on multiple devices to protect data privacy. Due to the diverse capacity of the …