FAdagrad: Adaptive federated learning with differential privacy

Federated Learning (FL) represents a promising distributed learning paradigm that enables model training without centralizing users' sensitive data. However, FL faces several practical challenges, such as communication overhead, convergence rates, robustness, and overall performance efficacy. A...

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Bibliographic Details

Published in:	2024 IEEE International Conference on High Performance Computing and Communications (HPCC) pp. 508 - 515
Main Authors:	Luo, Yuling, Pan, Ziyan, Fu, Qiang, Qin, Sheng
Format:	Conference Proceeding
Language:	English
Published:	IEEE 13.12.2024
Subjects:	Accuracy Adaptation models adaptive gradient descent algorithm Computational modeling Convergence Differential privacy Federated learning Privacy Protection Servers Training
Online Access:	Get full text
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