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Privacy Preserving

Papers

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Showing 551–600 of 2975 papers

TitleStatusHype
Federated Classification in Hyperbolic Spaces via Secure Aggregation of Convex HullsCode0
FedER: Federated Learning through Experience Replay and Privacy-Preserving Data SynthesisCode0
Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation SystemCode0
A Differentially Private Weighted Empirical Risk Minimization Procedure and its Application to Outcome Weighted LearningCode0
FedCTR: Federated Native Ad CTR Prediction with Multi-Platform User Behavior DataCode0
FedCAR: Cross-client Adaptive Re-weighting for Generative Models in Federated LearningCode0
Asynchronous Federated Learning: A Scalable Approach for Decentralized Machine LearningCode0
FedCAP: Robust Federated Learning via Customized Aggregation and PersonalizationCode0
Federated Causal Discovery From InterventionsCode0
FastLloyd: Federated, Accurate, Secure, and Tunable k-Means Clustering with Differential PrivacyCode0
Feasibility Study of Multi-Site Split Learning for Privacy-Preserving Medical Systems under Data Imbalance Constraints in COVID-19, X-Ray, and Cholesterol DatasetCode0
FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial LearningCode0
Dataset Condensation Driven Machine UnlearningCode0
FairFML: Fair Federated Machine Learning with a Case Study on Reducing Gender Disparities in Cardiac Arrest Outcome PredictionCode0
FedAH: Aggregated Head for Personalized Federated LearningCode0
Federated Learning for Time-Series Healthcare Sensing with Incomplete ModalitiesCode0
FedLAP-DP: Federated Learning by Sharing Differentially Private Loss ApproximationsCode0
Exploring Selective Layer Fine-Tuning in Federated LearningCode0
Exploring the Landscape for Generative Sequence Models for Specialized Data SynthesisCode0
Exploring Federated Pruning for Large Language ModelsCode0
Evaluating Privacy-Preserving Machine Learning in Critical Infrastructures: A Case Study on Time-Series ClassificationCode0
ER-AE: Differentially Private Text Generation for Authorship AnonymizationCode0
Estimating Model Performance on External Samples from Their Limited Statistical CharacteristicsCode0
Experimenting with Normalization Layers in Federated Learning on non-IID scenariosCode0
Data Augmentation Techniques for Cross-Domain WiFi CSI-based Human Activity RecognitionCode0
Enhancing Small Medical Learners with Privacy-preserving Contextual PromptingCode0
A Lightweight Privacy-Preserving Scheme Using Label-based Pixel Block Mixing for Image Classification in Deep LearningCode0
Dataset Distillation using Neural Feature RegressionCode0
DartBlur: Privacy Preservation With Detection Artifact SuppressionCode0
Enhanced Outsourced and Secure Inference for Tall Sparse Decision TreesCode0
Empowering Data Mesh with Federated LearningCode0
A Lightweight and Secure Deep Learning Model for Privacy-Preserving Federated Learning in Intelligent EnterprisesCode0
Enhancing Trade-offs in Privacy, Utility, and Computational Efficiency through MUltistage Sampling Technique (MUST)Code0
FADAS: Towards Federated Adaptive Asynchronous OptimizationCode0
Amalgam: A Framework for Obfuscated Neural Network Training on the CloudCode0
Decentralised, Collaborative, and Privacy-preserving Machine Learning for Multi-Hospital DataCode0
Efficient and Privacy-Preserved Link Prediction via Condensed GraphsCode0
FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated LearningCode0
DYSAN: Dynamically sanitizing motion sensor data against sensitive inferences through adversarial networksCode0
DRL-Based Resource Allocation for Motion Blur Resistant Federated Self-Supervised Learning in IoVCode0
DP-NMT: Scalable Differentially-Private Machine TranslationCode0
DPM: Clustering Sensitive Data through SeparationCode0
Cross-Network Social User Embedding with Hybrid Differential Privacy GuaranteesCode0
DPMAC: Differentially Private Communication for Cooperative Multi-Agent Reinforcement LearningCode0
DP-EM: Differentially Private Expectation MaximizationCode0
DP-Fast MH: Private, Fast, and Accurate Metropolis-Hastings for Large-Scale Bayesian InferenceCode0
Domain Adaptation from ScratchCode0
Domain Borders Are There to Be Crossed With Federated Few-Shot AdaptationCode0
DP-LSSGD: A Stochastic Optimization Method to Lift the Utility in Privacy-Preserving ERMCode0
DP-RTFL: Differentially Private Resilient Temporal Federated Learning for Trustworthy AI in Regulated IndustriesCode0
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