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Privacy Preserving

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Showing 451–475 of 2975 papers

TitleStatusHype
FedCAR: Cross-client Adaptive Re-weighting for Generative Models in Federated LearningCode0
UA-PDFL: A Personalized Approach for Decentralized Federated Learning—0
Learning Robust and Privacy-Preserving Representations via Information TheoryCode0
ProFe: Communication-Efficient Decentralized Federated Learning via Distillation and Prototypes—0
CENTAUR: Bridging the Impossible Trinity of Privacy, Efficiency, and Performance in Privacy-Preserving Transformer Inference—0
ScaleOT: Privacy-utility-scalable Offsite-tuning with Dynamic LayerReplace and Selective Rank Compression—0
(ε, δ)-Differentially Private Partial Least Squares Regression—0
Cluster-Enhanced Federated Graph Neural Network for Recommendation—0
Local Features Meet Stochastic Anonymization: Revolutionizing Privacy-Preserving Face Recognition for Black-Box Models—0
dsLassoCov: a federated machine learning approach incorporating covariate control—0
Protecting Confidentiality, Privacy and Integrity in Collaborative Learning—0
Federated In-Context LLM Agent Learning—0
Privacy-Preserving Customer Support: A Framework for Secure and Scalable Interactions—0
A New Federated Learning Framework Against Gradient Inversion AttacksCode1
A Framework for Privacy-Preserving in IoV Using Federated Learning With Differential Privacy—0
H-FedSN: Personalized Sparse Networks for Efficient and Accurate Hierarchical Federated Learning for IoT Applications—0
Privacy-Preserving Large Language Models: Mechanisms, Applications, and Future Directions—0
FedRBE -- a decentralized privacy-preserving federated batch effect correction tool for omics data based on limma—0
Can large language models be privacy preserving and fair medical coders?—0
Privacy-Preserving Retrieval-Augmented Generation with Differential Privacy—0
A Federated Approach to Few-Shot Hate Speech Detection for Marginalized Communities—0
Differentially Private Random Feature ModelCode0
A Differentially Private Kaplan-Meier Estimator for Privacy-Preserving Survival Analysis—0
Communication Compression for Distributed Learning without Control Variates—0
Federated Learning in Mobile Networks: A Comprehensive Case Study on Traffic ForecastingCode2
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