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Slingshot: Globally Favorable Local Updates for Federated Learning

Federated Learning (FL), as a promising distributed learning paradigm, is proposed to solve the contradiction between the data hunger of modern machine learning and the increasingly stringent need for data privacy. However, clients naturally present different distributions of their local data and in...

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Principais autores: Jialiang Liu, Huawei Huang, Chun Wang, Sicong Zhou, Ruixin Li, Zibin Zheng
פורמט: Artigo
שפה:Inglês
יצא לאור: IEEE 2024-01-01
סדרה:IEEE Open Journal of the Computer Society
נושאים:
Federated learning
data heterogeneity
catastrophic forgetting
model performance
גישה מקוונת:https://ieeexplore.ieee.org/document/10411043/
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