SOTAVerified

Privacy Preserving

Papers

Showing 19511975 of 2975 papers

TitleStatusHype
SwiftAgg+: Achieving Asymptotically Optimal Communication Loads in Secure Aggregation for Federated Learning0
Addressing Client Drift in Federated Continual Learning with Adaptive Optimization0
Privacy-Preserving Personalized Fitness Recommender System (P3FitRec): A Multi-level Deep Learning ApproachCode0
SPRITE: A Scalable Privacy-Preserving and Verifiable Collaborative Learning for Industrial IoT0
Federated Learning Approach for Lifetime Prediction of Semiconductor Lasers0
Privacy-Preserving Reinforcement Learning Beyond Expectation0
Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding AggregationCode1
Privacy-preserving Online AutoML for Domain-Specific Face Detection0
Privacy-Preserving Speech Representation Learning using Vector Quantization0
Generating Privacy-Preserving Process Data with Deep Generative Models0
Securing the Classification of COVID-19 in Chest X-ray Images: A Privacy-Preserving Deep Learning Approach0
Communication-Efficient Federated Distillation with Active Data Sampling0
Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation0
FedSyn: Synthetic Data Generation using Federated Learning0
No Free Lunch Theorem for Security and Utility in Federated Learning0
Federated Remote Physiological Measurement with Imperfect Data0
Similarity-based Label Inference Attack against Training and Inference of Split Learning0
Conditional Synthetic Data Generation for Personal Thermal Comfort Models0
Training privacy-preserving video analytics pipelines by suppressing features that reveal information about private attributesCode0
Faking feature importance: A cautionary tale on the use of differentially-private synthetic data0
Towards Efficient and Stable K-Asynchronous Federated Learning with Unbounded Stale Gradients on Non-IID Data0
FedREP: Towards Horizontal Federated Load Forecasting for Retail Energy Providers0
Estimating Model Performance on External Samples from Their Limited Statistical CharacteristicsCode0
Privacy-preserving machine learning with tensor networksCode0
How reparametrization trick broke differentially-private text representation learningCode0
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