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Privacy Preserving

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Showing 1351–1400 of 2975 papers

TitleStatusHype
A Survey of Graph Unlearning—0
Differentially Private Exploration in Reinforcement Learning with Linear Representation—0
Federated Knowledge Transfer Fine-tuning Large Server Model with Resource-Constrained IoT Clients—0
Gradient and Channel Aware Dynamic Scheduling for Over-the-Air Computation in Federated Edge Learning Systems—0
A Compressive Sensing Approach for Federated Learning over Massive MIMO Communication Systems—0
Differentially Private Federated Combinatorial Bandits with Constraints—0
Federated Knowledge Recycling: Privacy-Preserving Synthetic Data Sharing—0
Privacy-Preserving Taxi-Demand Prediction Using Federated Learning—0
Concentrated Differentially Private and Utility Preserving Federated Learning—0
Gradient Obfuscation Gives a False Sense of Security in Federated Learning—0
Federated Instruction Tuning of LLMs with Domain Coverage Augmentation—0
GradualDiff-Fed: A Federated Learning Specialized Framework for Large Language Model—0
AI-VERDE: A Gateway for Egalitarian Access to Large Language Model-Based Resources For Educational Institutions—0
Graph-Homomorphic Perturbations for Private Decentralized Learning—0
Gromov-Wasserstein Discrepancy with Local Differential Privacy for Distributed Structural Graphs—0
Grounding Foundation Models through Federated Transfer Learning: A General Framework—0
Bayesian Federated Learning: A Survey—0
GTV: Generating Tabular Data via Vertical Federated Learning—0
Guaranteed Privacy-Preserving H_-Optimal Interval Observer Design for Bounded-Error LTI Systems—0
Differentially Private GANs for Generating Synthetic Indoor Location Data—0
GuardNN: Secure Accelerator Architecture for Privacy-Preserving Deep Learning—0
Guided Persona-based AI Surveys: Can we replicate personal mobility preferences at scale using LLMs?—0
Differentially-Private Heat and Electricity Markets Coordination—0
Handling Data Heterogeneity with Generative Replay in Collaborative Learning for Medical Imaging—0
How Much Are You Willing to Share? A "Poker-Styled" Selective Privacy Preserving Framework for Recommender Systems—0
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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