#privacy-preserving3 articles

3 articles

AIBullisharXiv – CS AI · 4h ago4

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MPU: Towards Secure and Privacy-Preserving Knowledge Unlearning for Large Language Models

Researchers have developed MPU, a privacy-preserving framework that enables machine unlearning for large language models without requiring servers to share parameters or clients to share data. The framework uses perturbed model copies and harmonic denoising to achieve comparable performance to non-private methods, with most algorithms showing less than 1% performance degradation.

AIBullisharXiv – CS AI · 4h ago0

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Permutation-Invariant Representation Learning for Robust and Privacy-Preserving Feature Selection

Researchers have developed a new framework for privacy-preserving feature selection that uses permutation-invariant representation learning and federated learning techniques. The approach addresses data imbalance and privacy constraints in distributed scenarios while improving computational efficiency and downstream task performance.

AINeutralarXiv – CS AI · 4h ago1

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FedVG: Gradient-Guided Aggregation for Enhanced Federated Learning

Researchers introduce FedVG, a new federated learning framework that uses gradient-guided aggregation and global validation sets to improve model performance in distributed training environments. The approach addresses client drift issues in heterogeneous data settings and can be integrated with existing federated learning algorithms.