SOTAVerified

Privacy Preserving

Papers

Showing 276–300 of 2975 papers

TitleStatusHype
DistFL: Distribution-aware Federated Learning for Mobile ScenariosCode1
Domain-Invariant Representation Learning from EEG with Private EncodersCode1
DP-OPT: Make Large Language Model Your Privacy-Preserving Prompt EngineerCode1
Concentrated Differential Privacy: Simplifications, Extensions, and Lower BoundsCode1
Defending against Backdoors in Federated Learning with Robust Learning RateCode1
EViT: Privacy-Preserving Image Retrieval via Encrypted Vision Transformer in Cloud ComputingCode1
Fedlearn-Algo: A flexible open-source privacy-preserving machine learning platformCode1
MMASD+: A Novel Dataset for Privacy-Preserving Behavior Analysis of Children with Autism Spectrum DisorderCode1
A Secure and Efficient Distributed Semantic Communication System for Heterogeneous Internet of Things—0
A Scalable Approach for Privacy-Preserving Collaborative Machine Learning—0
AI and Democracy's Digital Identity Crisis—0
A Safe Deep Reinforcement Learning Approach for Energy Efficient Federated Learning in Wireless Communication Networks—0
Artificial Intelligence for Secured Information Systems in Smart Cities: Collaborative IoT Computing with Deep Reinforcement Learning and Blockchain—0
A Hybrid Federated Kernel Regularized Least Squares Algorithm—0
Additively Homomorphical Encryption based Deep Neural Network for Asymmetrically Collaborative Machine Learning—0
Artificial Intelligence at the Edge—0
Artemis: HE-Aware Training for Efficient Privacy-Preserving Machine Learning—0
Large-Scale Secure XGB for Vertical Federated Learning—0
Identifying Backdoor Attacks in Federated Learning via Anomaly Detection—0
Additive Logistic Mechanism for Privacy-Preserving Self-Supervised Learning—0
Accelerating Federated Edge Learning via Optimized Probabilistic Device Scheduling—0
Bayesian Federated Learning: A Survey—0
Bayesian Federated Learning over Wireless Networks—0
A Review on Machine Unlearning—0
Adaptive Hybrid Masking Strategy for Privacy-Preserving Face Recognition Against Model Inversion Attack—0
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