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Privacy Preserving

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Showing 1051–1100 of 2975 papers

TitleStatusHype
Share Secrets for Privacy: Confidential Forecasting with Vertical Federated LearningCode0
No Free Lunch Theorem for Privacy-Preserving LLM Inference—0
GANcrop: A Contrastive Defense Against Backdoor Attacks in Federated Learning—0
Gradient Inversion of Federated Diffusion Models—0
Just Rewrite It Again: A Post-Processing Method for Enhanced Semantic Similarity and Privacy Preservation of Differentially Private Rewritten Text—0
Exploring the Practicality of Federated Learning: A Survey Towards the Communication Perspective—0
Enhancing Security and Privacy in Federated Learning using Low-Dimensional Update Representation and Proximity-Based Defense—0
Privacy Preserving Data Imputation via Multi-party Computation for Medical Applications—0
LabObf: A Label Protection Scheme for Vertical Federated Learning Through Label Obfuscation—0
Anonymization Prompt Learning for Facial Privacy-Preserving Text-to-Image Generation—0
Privacy and Security Trade-off in Interconnected Systems with Known or Unknown Privacy Noise Covariance—0
FedHPL: Efficient Heterogeneous Federated Learning with Prompt Tuning and Logit Distillation—0
Client2Vec: Improving Federated Learning by Distribution Shifts Aware Client IndexingCode0
Noisy Data Meets Privacy: Training Local Models with Post-Processed Remote Queries—0
Comet: A Communication-efficient and Performant Approximation for Private Transformer Inference—0
PriCE: Privacy-Preserving and Cost-Effective Scheduling for Parallelizing the Large Medical Image Processing Workflow over Hybrid CloudsCode0
Privacy-preserving recommender system using the data collaboration analysis for distributed datasets—0
Scaling up the Banded Matrix Factorization Mechanism for Differentially Private ML—0
A Systematic and Formal Study of the Impact of Local Differential Privacy on Fairness: Preliminary Results—0
AdaFedFR: Federated Face Recognition with Adaptive Inter-Class Representation Learning—0
AdpQ: A Zero-shot Calibration Free Adaptive Post Training Quantization Method for LLMs—0
Task-agnostic Decision Transformer for Multi-type Agent Control with Federated Split Training—0
Federated Learning for Time-Series Healthcare Sensing with Incomplete ModalitiesCode0
StatAvg: Mitigating Data Heterogeneity in Federated Learning for Intrusion Detection Systems—0
FeMLoc: Federated Meta-learning for Adaptive Wireless Indoor Localization Tasks in IoT Networks—0
Air Signing and Privacy-Preserving Signature Verification for Digital DocumentsCode0
Multicenter Privacy-Preserving Model Training for Deep Learning Brain Metastases AutosegmentationCode0
The Effect of Quantization in Federated Learning: A Rényi Differential Privacy Perspective—0
Advances in Robust Federated Learning: Heterogeneity Considerations—0
Dealing Doubt: Unveiling Threat Models in Gradient Inversion Attacks under Federated Learning, A Survey and Taxonomy—0
Byzantine-Resilient Secure Aggregation for Federated Learning Without Privacy Compromises—0
Differentially Private Federated Learning: A Systematic Review—0
Mitigating federated learning contribution allocation instability through randomized aggregation—0
Navigating the Future of Federated Recommendation Systems with Foundation Models—0
Privacy-Preserving Edge Federated Learning for Intelligent Mobile-Health SystemsCode0
An Inversion-based Measure of Memorization for Diffusion ModelsCode0
Communication-efficient and Differentially-private Distributed Nash Equilibrium Seeking with Linear Convergence—0
Scalable Vertical Federated Learning via Data Augmentation and Amortized Inference—0
IPFed: Identity protected federated learning for user authentication—0
A2-DIDM: Privacy-preserving Accumulator-enabled Auditing for Distributed Identity of DNN Model—0
Federated Graph Condensation with Information Bottleneck Principles—0
The Federation Strikes Back: A Survey of Federated Learning Privacy Attacks, Defenses, Applications, and Policy Landscape—0
GI-SMN: Gradient Inversion Attack against Federated Learning without Prior Knowledge—0
Differentially Private Federated Learning without Noise Addition: When is it Possible?—0
FastLloyd: Federated, Accurate, Secure, and Tunable k-Means Clustering with Differential PrivacyCode0
1-Diffractor: Efficient and Utility-Preserving Text Obfuscation Leveraging Word-Level Metric Differential PrivacyCode0
Enhancing Mutual Trustworthiness in Federated Learning for Data-Rich Smart Cities—0
Bridging Data Barriers among Participants: Assessing the Potential of Geoenergy through Federated Learning—0
A Universal Metric of Dataset Similarity for Cross-silo Federated LearningCode0
Privacy-Preserving Aggregation for Decentralized Learning with Byzantine-Robustness—0
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