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Privacy Preserving

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Showing 776–800 of 2975 papers

TitleStatusHype
Dual-Criterion Model Aggregation in Federated Learning: Balancing Data Quantity and Quality—0
Which PPML Would a User Choose? A Structured Decision Support Framework for Developers to Rank PPML Techniques Based on User Acceptance Criteria—0
Are Neuromorphic Architectures Inherently Privacy-preserving? An Exploratory Study—0
PRISM: Privacy-preserving Inter-Site MRI Harmonization via Disentangled Representation LearningCode0
In-Context Learning for Preserving Patient Privacy: A Framework for Synthesizing Realistic Patient Portal MessagesCode0
A capacity renting framework for shared energy storage considering peer-to-peer energy trading of prosumers with privacy protection—0
TinyML NLP Scheme for Semantic Wireless Sentiment Classification with Privacy PreservationCode0
Differential Privacy Under Class Imbalance: Methods and Empirical Insights—0
ViT Enhanced Privacy-Preserving Secure Medical Data Sharing and Classification—0
Uncovering Hidden Subspaces in Video Diffusion Models Using Re-Identification—0
Personalized Federated Learning for Cross-view Geo-localization—0
DWFL: Enhancing Federated Learning through Dynamic Weighted Averaging—0
Differential Privacy Overview and Fundamental Techniques—0
FedDP: Privacy-preserving method based on federated learning for histopathology image segmentation—0
EPIC: Enhancing Privacy through Iterative Collaboration—0
Towards Resource-Efficient Federated Learning in Industrial IoT for Multivariate Time Series Analysis—0
Privacy-Preserving Resilient Vector Consensus—0
Privacy Preserving Mechanisms for Coordinating Airspace Usage in Advanced Air Mobility—0
NeurIPS 2023 Competition: Privacy Preserving Federated Learning Document VQA—0
FEDLAD: Federated Evaluation of Deep Leakage Attacks and Defenses—0
User-wise Perturbations for User Identity Protection in EEG-Based BCIs—0
R+R:Understanding Hyperparameter Effects in DP-SGD—0
Differentially private and decentralized randomized power method—0
Pseudo-Probability Unlearning: Towards Efficient and Privacy-Preserving Machine Unlearning—0
Exploring the Landscape for Generative Sequence Models for Specialized Data SynthesisCode0
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