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Privacy Preserving

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Showing 2101–2150 of 2975 papers

TitleStatusHype
Parameter-free HE-friendly Logistic Regression—0
Partial Disclosure of Private Dependencies in Privacy Preserving Planning—0
PartialFed: Cross-Domain Personalized Federated Learning via Partial Initialization—0
Democratizing Differential Privacy: A Participatory AI Framework for Public Decision-Making—0
PATE-AAE: Incorporating Adversarial Autoencoder into Private Aggregation of Teacher Ensembles for Spoken Command Classification—0
PATE-TripleGAN: Privacy-Preserving Image Synthesis with Gaussian Differential Privacy—0
PCAL: A Privacy-preserving Intelligent Credit Risk Modeling Framework Based on Adversarial Learning—0
PDC-FRS: Privacy-preserving Data Contribution for Federated Recommender System—0
PDFed: Privacy-Preserving and Decentralized Asynchronous Federated Learning for Diffusion Models—0
PDSR: A Privacy-Preserving Diversified Service Recommendation Method on Distributed Data—0
PDSS: A Privacy-Preserving Framework for Step-by-Step Distillation of Large Language Models—0
PEBench: A Fictitious Dataset to Benchmark Machine Unlearning for Multimodal Large Language Models—0
Peer-to-Peer Learning Dynamics of Wide Neural Networks—0
PEFT-as-an-Attack! Jailbreaking Language Models during Federated Parameter-Efficient Fine-Tuning—0
PerFedRec++: Enhancing Personalized Federated Recommendation with Self-Supervised Pre-Training—0
User-Level Privacy-Preserving Federated Learning: Analysis and Performance Optimization—0
Federated Learning with Differential Privacy: Algorithms and Performance Analysis—0
Performance Weighting for Robust Federated Learning Against Corrupted Sources—0
Personalized Federated Learning for Cross-view Geo-localization—0
Personalized Federated Learning via Amortized Bayesian Meta-Learning—0
Personalized Federated Learning with Adaptive Feature Aggregation and Knowledge Transfer—0
Personalized Federated Learning with Hidden Information on Personalized Prior—0
Personalized Graph Federated Learning with Differential Privacy—0
Dynamic Fusion Strategies for Federated Multimodal Recommendations—0
Personalized Language Models via Privacy-Preserving Evolutionary Model Merging—0
Personalized Speech Enhancement through Self-Supervised Data Augmentation and Purification—0
Personalizing Federated Learning with Over-the-Air Computations—0
Personalizing Pre-trained Models—0
pFedES: Model Heterogeneous Personalized Federated Learning with Feature Extractor Sharing—0
pFedMxF: Personalized Federated Class-Incremental Learning with Mixture of Frequency Aggregation—0
Physical Layer Anonymous Precoding: The Path to Privacy-Preserving Communications—0
Physical Trajectory Inference Attack and Defense in Decentralized POI Recommendation—0
PicoSAM2: Low-Latency Segmentation In-Sensor for Edge Vision Applications—0
PINFER: Privacy-Preserving Inference for Machine Learning—0
PiPar: Pipeline Parallelism for Collaborative Machine Learning—0
PIRNet: Privacy-Preserving Image Restoration Network via Wavelet Lifting—0
PIVODL: Privacy-preserving vertical federated learning over distributed labels—0
Plankton-FL: Exploration of Federated Learning for Privacy-Preserving Training of Deep Neural Networks for Phytoplankton Classification—0
PlateSegFL: A Privacy-Preserving License Plate Detection Using Federated Segmentation Learning—0
Plausible Deniability for Privacy-Preserving Data Synthesis—0
PluralLLM: Pluralistic Alignment in LLMs via Federated Learning—0
PockEngine: Sparse and Efficient Fine-tuning in a Pocket—0
Poincaré Differential Privacy for Hierarchy-Aware Graph Embedding—0
Poisoning Attacks in Federated Edge Learning for Digital Twin 6G-enabled IoTs: An Anticipatory Study—0
PoLO: Proof-of-Learning and Proof-of-Ownership at Once with Chained Watermarking—0
PolyMPCNet: Towards ReLU-free Neural Architecture Search in Two-party Computation Based Private Inference—0
POSEIDON: Privacy-Preserving Federated Neural Network Learning—0
Pose Uncertainty Aware Movement Synchrony Estimation via Spatial-Temporal Graph Transformer—0
Position: Federated Foundation Language Model Post-Training Should Focus on Open-Source Models—0
Power-Softmax: Towards Secure LLM Inference over Encrypted Data—0
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