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Privacy Preserving

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Showing 1401–1425 of 2975 papers

TitleStatusHype
Exploring the Impact of Disrupted Peer-to-Peer Communications on Fully Decentralized Learning in Disaster Scenarios—0
Shielding the Unseen: Privacy Protection through Poisoning NeRF with Spatial Deformation—0
Privacy-preserving Multi-biometric Indexing based on Frequent Binary Patterns—0
The Inhibitor: ReLU and Addition-Based Attention for Efficient Transformers—0
Artemis: HE-Aware Training for Efficient Privacy-Preserving Machine Learning—0
FedBPT: Efficient Federated Black-box Prompt Tuning for Large Language Models—0
Bridging the Gap Between Foundation Models and Heterogeneous Federated Learning—0
Beyond Random Noise: Insights on Anonymization Strategies from a Latent Bandit Study—0
Privacy-Preserving Distributed Market Mechanism for Active Distribution Networks—0
SAppKG: Mobile App Recommendation Using Knowledge Graph and Side Information-A Secure FrameworkCode0
Building Privacy-Preserving and Secure Geospatial Artificial Intelligence Foundation Models—0
FedZeN: Towards superlinear zeroth-order federated learning via incremental Hessian estimation—0
Enabling Differentially Private Federated Learning for Speech Recognition: Benchmarks, Adaptive Optimizers and Gradient Clipping—0
A Unified View of Differentially Private Deep Generative Modeling—0
AI and Democracy's Digital Identity Crisis—0
Privacy-preserving Linear Computations in Spiking Neural P Systems—0
Guaranteed Privacy-Preserving H_-Optimal Interval Observer Design for Bounded-Error LTI Systems—0
Federated Short-Term Load Forecasting with Personalization Layers for Heterogeneous Clients—0
Multiply Robust Federated Estimation of Targeted Average Treatment Effects—0
S-BDT: Distributed Differentially Private Boosted Decision TreesCode0
Methods for generating and evaluating synthetic longitudinal patient data: a systematic review—0
Preconditioned Federated Learning—0
Striking a Balance: An Optimal Mechanism Design for Heterogenous Differentially Private Data Acquisition for Logistic Regression—0
DPpack: An R Package for Differentially Private Statistical Analysis and Machine Learning—0
FRAMU: Attention-based Machine Unlearning using Federated Reinforcement Learning—0
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