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Privacy Preserving

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Showing 1451–1475 of 2975 papers

TitleStatusHype
An Attribute-Aligned Strategy for Learning Speech Representation—0
Improving Correlation Capture in Generating Imbalanced Data using Differentially Private Conditional GANs—0
FFA Sora, video generation as fundus fluorescein angiography simulator—0
Improving Federated Relational Data Modeling via Basis Alignment and Weight Penalty—0
Few-shot Image Recognition with Manifolds—0
Improving Multi-Center Generalizability of GAN-Based Fat Suppression using Federated Learning—0
Demystifying Hyperparameter Optimization in Federated Learning—0
Federated Learning with Heterogeneous Differential Privacy—0
Demo: LE3D: A Privacy-preserving Lightweight Data Drift Detection Framework—0
In-Bed Human Pose Estimation from Unseen and Privacy-Preserving Image Domains—0
A Unified View of Differentially Private Deep Generative Modeling—0
FeMLoc: Federated Meta-learning for Adaptive Wireless Indoor Localization Tasks in IoT Networks—0
Incentivizing Multi-Tenant Split Federated Learning for Foundation Models at the Network Edge—0
FELLAS: Enhancing Federated Sequential Recommendation with LLM as External Services—0
FedZeN: Towards superlinear zeroth-order federated learning via incremental Hessian estimation—0
IndicFed: A Federated Approach for Sentiment Analysis in Indic Languages—0
FedXGBoost: Privacy-Preserving XGBoost for Federated Learning—0
A Unified Learn-to-Distort-Data Framework for Privacy-Utility Trade-off in Trustworthy Federated Learning—0
An Attack to Break Permutation-Based Private Third-Party Inference Schemes for LLMs—0
Inference Attacks Against Face Recognition Model without Classification Layers—0
AdRo-FL: Informed and Secure Client Selection for Federated Learning in the Presence of Adversarial Aggregator—0
Inferring Intentions to Speak Using Accelerometer Data In-the-Wild—0
FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Models—0
Information Theoretic Evaluation of Privacy-Leakage, Interpretability, and Transferability for Trustworthy AI—0
FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Data—0
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