SOTAVerified

Privacy Preserving

Papers

Showing 251300 of 2975 papers

TitleStatusHype
FedVLN: Privacy-preserving Federated Vision-and-Language NavigationCode1
Federated Recommendation via Hybrid Retrieval Augmented GenerationCode1
Position: Considerations for Differentially Private Learning with Large-Scale Public PretrainingCode1
DataLens: Scalable Privacy Preserving Training via Gradient Compression and AggregationCode1
FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated LearningCode1
FedTP: Federated Learning by Transformer PersonalizationCode1
Fully Homomorphically Encrypted Deep Learning as a ServiceCode1
FedIIC: Towards Robust Federated Learning for Class-Imbalanced Medical Image ClassificationCode1
FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated LearningCode1
Fed-MUnet: Multi-modal Federated Unet for Brain Tumor SegmentationCode1
FedNoRo: Towards Noise-Robust Federated Learning by Addressing Class Imbalance and Label Noise HeterogeneityCode1
FedSim: Similarity guided model aggregation for Federated LearningCode1
Fedlearn-Algo: A flexible open-source privacy-preserving machine learning platformCode1
Fed-TGAN: Federated Learning Framework for Synthesizing Tabular DataCode1
Attacks on Image Encryption Schemes for Privacy-Preserving Deep Neural NetworksCode1
FLASHE: Additively Symmetric Homomorphic Encryption for Cross-Silo Federated LearningCode1
FedMatch: Federated Learning Over Heterogeneous Question Answering DataCode1
A Survey for Federated Learning Evaluations: Goals and MeasuresCode1
Exploring Federated Unlearning: Review, Comparison, and InsightsCode1
Generalizable Heterogeneous Federated Cross-Correlation and Instance Similarity LearningCode1
A Survey of Privacy-Preserving Model Explanations: Privacy Risks, Attacks, and CountermeasuresCode1
Can Foundation Models Help Us Achieve Perfect Secrecy?Code1
Attack-Aware Noise Calibration for Differential PrivacyCode1
Can We Use Split Learning on 1D CNN Models for Privacy Preserving Training?Code1
FedHCDR: Federated Cross-Domain Recommendation with Hypergraph Signal DecouplingCode1
FedSIS: Federated Split Learning with Intermediate Representation Sampling for Privacy-preserving Generalized Face Presentation Attack DetectionCode1
CipherPrune: Efficient and Scalable Private Transformer InferenceCode1
Gradient-Leakage Resilient Federated LearningCode1
Language-Guided Transformer for Federated Multi-Label ClassificationCode1
On the Utility Gain of Iterative Bayesian Update for Locally Differentially Private MechanismsCode1
ID-Booth: Identity-consistent Face Generation with Diffusion ModelsCode1
PromptEHR: Conditional Electronic Healthcare Records Generation with Prompt LearningCode1
A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and ApplicationsCode1
Federated Split GANsCode0
Federated Spectral Graph Transformers Meet Neural Ordinary Differential Equations for Non-IID GraphsCode0
Federated Stain Normalization for Computational PathologyCode0
FedPCL-CDR: A Federated Prototype-based Contrastive Learning Framework for Privacy-Preserving Cross-domain RecommendationCode0
Federated Semantic Learning for Privacy-preserving Cross-domain RecommendationCode0
Federated Survival ForestsCode0
Federated Motor Imagery Classification for Privacy-Preserving Brain-Computer InterfacesCode0
A Hybrid Deep Learning Architecture for Privacy-Preserving Mobile AnalyticsCode0
ARIA: On the Interaction Between Architectures, Initialization and Aggregation Methods for Federated Visual ClassificationCode0
A Hybrid Approach to Privacy-Preserving Federated LearningCode0
Federated Learning for Time-Series Healthcare Sensing with Incomplete ModalitiesCode0
Federated Causal Inference from Observational DataCode0
Federated Learning in Chemical Engineering: A Tutorial on a Framework for Privacy-Preserving Collaboration Across Distributed Data SourcesCode0
1-Diffractor: Efficient and Utility-Preserving Text Obfuscation Leveraging Word-Level Metric Differential PrivacyCode0
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and ProtectionCode0
Federated Learning with Reduced Information Leakage and ComputationCode0
Federated Unlearning Made Practical: Seamless Integration via Negated Pseudo-GradientsCode0
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