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Privacy Preserving

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Showing 601–625 of 2975 papers

TitleStatusHype
Privately Learning from Graphs with Applications in Fine-tuning Large Language ModelsCode0
MIRACLE 3D: Memory-efficient Integrated Robust Approach for Continual Learning on Point Clouds via Shape Model construction—0
De-VertiFL: A Solution for Decentralized Vertical Federated Learning—0
KnowledgeSG: Privacy-Preserving Synthetic Text Generation with Knowledge Distillation from ServerCode0
Federated brain tumor segmentation: an extensive benchmark—0
FairFML: Fair Federated Machine Learning with a Case Study on Reducing Gender Disparities in Cardiac Arrest Outcome PredictionCode0
Federated Learning Nodes Can Reconstruct Peers' Image Data—0
FELLAS: Enhancing Federated Sequential Recommendation with LLM as External Services—0
A Global Medical Data Security and Privacy Preserving Standards Identification Framework for Electronic Healthcare Consumers—0
A Survey on Point-of-Interest Recommendation: Models, Architectures, and Security—0
Encryption-Friendly LLM ArchitectureCode1
TAEGAN: Generating Synthetic Tabular Data For Data Augmentation—0
Thinking Outside of the Differential Privacy Box: A Case Study in Text Privatization with Language Model PromptingCode0
Differentially Private Active Learning: Balancing Effective Data Selection and PrivacyCode0
FedPT: Federated Proxy-Tuning of Large Language Models on Resource-Constrained Edge Devices—0
Federated Instruction Tuning of LLMs with Domain Coverage Augmentation—0
Fast-Convergent and Communication-Alleviated Heterogeneous Hierarchical Federated Learning in Autonomous Driving—0
Heterogeneity-Aware Resource Allocation and Topology Design for Hierarchical Federated Edge Learning—0
Convergence-aware Clustered Federated Graph Learning Framework for Collaborative Inter-company Labor Market Forecasting—0
Confidential Prompting: Protecting User Prompts from Cloud LLM ProvidersCode0
Differentially Private Non Parametric Copulas: Generating synthetic data with non parametric copulas under privacy guarantees—0
FedDCL: a federated data collaboration learning as a hybrid-type privacy-preserving framework based on federated learning and data collaboration—0
PDFed: Privacy-Preserving and Decentralized Asynchronous Federated Learning for Diffusion Models—0
Development of an Edge Resilient ML Ensemble to Tolerate ICS Adversarial Attacks—0
Trustworthy AI: Securing Sensitive Data in Large Language Models—0
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