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Privacy Preserving

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Showing 1376–1400 of 2975 papers

TitleStatusHype
Federated In-Context LLM Agent Learning—0
Harnessing Wireless Channels for Scalable and Privacy-Preserving Federated Learning—0
Harvesting Private Medical Images in Federated Learning Systems with Crafted Models—0
Hashmarks: Privacy-Preserving Benchmarks for High-Stakes AI Evaluation—0
Hawk: Accurate and Fast Privacy-Preserving Machine Learning Using Secure Lookup Table Computation—0
HCFContext: Smartphone Context Inference via Sequential History-based Collaborative Filtering—0
hear-your-action: human action recognition by ultrasound active sensing—0
Federated Heterogeneous Graph Neural Network for Privacy-preserving Recommendation—0
HEMET: A Homomorphic-Encryption-Friendly Privacy-Preserving Mobile Neural Network Architecture—0
Efficient Pruning for Machine Learning Under Homomorphic Encryption—0
Here Is Not There: Measuring Entailment-Based Trajectory Similarity for Location-Privacy Protection and Beyond—0
Addressing the cold start problem in privacy preserving content-based recommender systems using hypercube graphs—0
Characterizing Impacts of Heterogeneity in Federated Learning upon Large-Scale Smartphone Data—0
Heterogeneity-Aware Resource Allocation and Topology Design for Hierarchical Federated Edge Learning—0
Heterogeneous Federated Learning via Grouped Sequential-to-Parallel Training—0
Heterogeneous Federated Learning with Convolutional and Spiking Neural Networks—0
Federated Graph Learning with Adaptive Importance-based Sampling—0
Heterogeneous LoRA for Federated Fine-tuning of On-Device Foundation Models—0
Differentially Private Matrix Completion Revisited—0
H-FedSN: Personalized Sparse Networks for Efficient and Accurate Hierarchical Federated Learning for IoT Applications—0
H-FLTN: A Privacy-Preserving Hierarchical Framework for Electric Vehicle Spatio-Temporal Charge Prediction—0
Federated Graph Learning for Low Probability of Detection in Wireless Ad-Hoc Networks—0
FLBench: A Benchmark Suite for Federated Learning—0
Differentially Private M-Estimators—0
How Does Knowledge of the AUC Constrain the Set of Possible Ground-truth Labelings?—0
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