FLRSP enhances privacy in federated learning by randomly selecting model parameters for sharing, delivering competitive image classification accuracy and improved resistance to reconstruction attacks on ResNet34 and ViT models using FedSGD and FedAvg.
Federated learning with differen- tial privacy: Algorithms and performance analysis
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FLRSP: Privacy-Preserving Federated Learning Using Randomly Selected Model Parameters
FLRSP enhances privacy in federated learning by randomly selecting model parameters for sharing, delivering competitive image classification accuracy and improved resistance to reconstruction attacks on ResNet34 and ViT models using FedSGD and FedAvg.