A Novel Approach for Differential Privacy-Preserving Federated Learning
In this paper, we start with a comprehensive evaluation of the effect of adding differential privacy (DP) to federated learning (FL) approaches, focusing on methodologies employing global (stochastic) gradient descent (SGD/GD), and local SGD/GD techniques. These global and local techniques are commo...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Open Journal of the Communications Society |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10812948/ |
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