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Privacy Preserving

Papers

Showing 176200 of 2975 papers

TitleStatusHype
Privacy-Preserving Action Recognition via Motion Difference QuantizationCode1
Towards Privacy-Preserving, Real-Time and Lossless Feature MatchingCode1
Learnable Privacy-Preserving Anonymization for Pedestrian ImagesCode1
POET: Training Neural Networks on Tiny Devices with Integrated Rematerialization and PagingCode1
FedIIC: Towards Robust Federated Learning for Class-Imbalanced Medical Image ClassificationCode1
FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated LearningCode1
A federated graph neural network framework for privacy-preserving personalizationCode1
Can Foundation Models Help Us Achieve Perfect Secrecy?Code1
GeoPointGAN: Synthetic Spatial Data with Local Label Differential PrivacyCode1
Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical ImagingCode1
Privacy-Preserving Model Upgrades with Bidirectional Compatible Training in Image RetrievalCode1
Federated Learning on Heterogeneous and Long-Tailed Data via Classifier Re-Training with Federated FeaturesCode1
Exploring the Distributed Knowledge Congruence in Proxy-data-free Federated DistillationCode1
FedCorr: Multi-Stage Federated Learning for Label Noise CorrectionCode1
Production of Categorical Data Verifying Differential Privacy: Conception and Applications to Machine LearningCode1
CTAB-GAN+: Enhancing Tabular Data SynthesisCode1
Rethinking Portrait Matting with Privacy PreservingCode1
Neural Inertial LocalizationCode1
FedVLN: Privacy-preserving Federated Vision-and-Language NavigationCode1
Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding AggregationCode1
OLIVE: Oblivious Federated Learning on Trusted Execution Environment against the risk of sparsificationCode1
UA-FedRec: Untargeted Attack on Federated News RecommendationCode1
APPFL: Open-Source Software Framework for Privacy-Preserving Federated LearningCode1
Privacy-preserving Speech Emotion Recognition through Semi-Supervised Federated LearningCode1
Domain-Invariant Representation Learning from EEG with Private EncodersCode1
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