UMEDA is a new graph federated learning method that uses low-rank spectral filtering and diffusion over a shared integral operator to fuse multi-modal data privately, outperforming baselines on MM-Fi and RELI11D under high heterogeneity and tight privacy budgets.
Differentially private federated learning with time-adaptive privacy spending
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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.
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UMEDA: Unified Multi-modal Efficient Data Fusion for Privacy-Preserving Graph Federated Learning via Spectral-Gated Attention and Diffusion-Based Operator Alignment
UMEDA is a new graph federated learning method that uses low-rank spectral filtering and diffusion over a shared integral operator to fuse multi-modal data privately, outperforming baselines on MM-Fi and RELI11D under high heterogeneity and tight privacy budgets.
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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.