Latest Articles for Data Privacy

Synthetic data offers promising solutions to challenges in medical imaging.

Exploring AI's impact on efficiency, privacy, and security in vital systems.

A new method lowers communication loads in federated learning while enhancing privacy.

A new method enhances prediction accuracy while protecting user privacy in cloud models.

Client2Vec enhances federated learning by creating unique identifiers for user data.

A new method improves jailbreak attacks on advanced AI models using character role-play.

Discover a method for decentralized optimization that protects user data while improving efficiency.

Exploring the blend of privacy-focused learning and data generation techniques.

Learn how AltGDmin addresses missing data in a federated setting.

A new method to improve federated learning through Multi-Level Additive Modeling.

Exploring new ways to ensure privacy in wireless communication.

A new method improves fairness in federated learning across diverse communities.

Training open-source LLMs enhances optimization modeling for industry applications.

Harnessing early-exit models for efficient federated learning in ASR systems.

Explore the vital role of access control in edge computing.

FedHPL enhances federated learning efficiency while ensuring data privacy across devices.

A new method enables the transfer of LoRA modules with synthetic data, minimizing reliance on original data.

This study improves the security of quantum machine learning against adversarial attacks through noise channels and privacy methods.

Fast-FedUL provides quick data removal methods for federated learning while ensuring privacy.

New methods enhance data removal processes in machine learning models.

KALM4Rec enhances recommendations for new users using keyword-driven methods.

Learn how data minimization protects privacy amidst growing data collection.

New methods reduce memorization risks in medical imaging with diffusion models.

FeMLoc improves indoor localization using federated and meta-learning techniques.

This article discusses differential privacy's impact on trust in the U.S. Census.

A new method to enhance exemplar-free continual learning by tracking class representation changes.

FedGELA tackles challenges in federated learning with partially class-disjoint data.

A new method for federated learning that addresses continual learning challenges.

FedLESAM tackles data challenges in federated learning for better model performance.

Exploring methods to secure patient information in clinical research.

FedMR tackles challenges in federated learning with partial class data, enhancing model performance.

PadFL improves model sharing and efficiency across varying device capabilities.

Federated Learning trains models while keeping user data private and secure.

AI tools support negotiators in complex environments, enhancing efficiency and decision-making.

A method to rewrite texts while protecting individuals' privacy.

A new approach enhances security and performance in federated learning with blockchain.

Study reveals how mobile apps shape travel behavior insights.

Examining client contributions and vulnerabilities in federated learning systems.

A method for collaborative machine learning while keeping data private.

New methods for privacy-preserving collaboration in vertical federated learning.