Imagen de Portada

Deep Efficient Private Neighbor Generation for Subgraph Federated Learning

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Zhang, Ke, Sun, Lichao, Ding, Bolin, Yiu, Siu Ming, Yang, Carl
Formato:	Preprint
Publicado:	2024
Materias:	Machine Learning Artificial Intelligence Cryptography and Security
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Geminio: Language-Guided Gradient Inversion Attacks in Federated Learning
por: Shan, Junjie, et al.
Publicado: (2024)

Differentially Private In-Context Learning with Nearest Neighbor Search
por: Koskela, Antti, et al.
Publicado: (2025)

FedCAP: Robust Federated Learning via Customized Aggregation and Personalization
por: Li, Youpeng, et al.
Publicado: (2024)

PM-MOE: Mixture of Experts on Private Model Parameters for Personalized Federated Learning
por: Feng, Yu, et al.
Publicado: (2025)

Privacy Assessment of Federated Learning using Private Personalized Layers
por: Jourdan, Théo, et al.
Publicado: (2021)

Privacy-Preserving Federated Learning with Differentially Private Hyperdimensional Computing
por: Piran, Fardin Jalil, et al.
Publicado: (2024)

Differentially Private Densest Subgraph Detection
por: Nguyen, Dung, et al.
Publicado: (2021)

Security Assessment of Hierarchical Federated Deep Learning
por: Alqattan, D, et al.
Publicado: (2024)

Differentially Private Deep Model-Based Reinforcement Learning
por: Rio, Alexandre, et al.
Publicado: (2024)

LAN: Learning Adaptive Neighbors for Real-Time Insider Threat Detection
por: Cai, Xiangrui, et al.
Publicado: (2024)

Differentially Private Block-wise Gradient Shuffle for Deep Learning
por: Zagardo, David
Publicado: (2024)

FERRET: Private Deep Learning Faster And Better Than DPSGD
por: Zagardo, David
Publicado: (2025)

Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off
por: Li, Yuecheng, et al.
Publicado: (2024)

Multi-Continental Healthcare Modelling Using Blockchain-Enabled Federated Learning
por: Sun, Rui, et al.
Publicado: (2024)

Provable Differentially Private Computation of the Cross-Attention Mechanism
por: Ke, Yekun, et al.
Publicado: (2024)

Beyond Data Privacy: New Privacy Risks for Large Language Models
por: Du, Yuntao, et al.
Publicado: (2025)

Evaluating Differentially Private Generation of Domain-Specific Text
por: Sun, Yidan, et al.
Publicado: (2025)

Exploring Query Efficient Data Generation towards Data-free Model Stealing in Hard Label Setting
por: Pei, Gaozheng, et al.
Publicado: (2024)

Structure-Aware Distributed Backdoor Attacks in Federated Learning
por: Jian, Wang, et al.
Publicado: (2026)

How Does a Deep Learning Model Architecture Impact Its Privacy? A Comprehensive Study of Privacy Attacks on CNNs and Transformers
por: Zhang, Guangsheng, et al.
Publicado: (2022)

A Robust Framework for Secure Cardiovascular Risk Prediction: An Architectural Case Study of Differentially Private Federated Learning
por: Tertulino, Rodrigo, et al.
Publicado: (2026)

Towards Achieving Near-optimal Utility for Privacy-Preserving Federated Learning via Data Generation and Parameter Distortion
por: Zhang, Xiaojin, et al.
Publicado: (2023)

GraMFedDHAR: Graph Based Multimodal Differentially Private Federated HAR
por: Halder, Labani, et al.
Publicado: (2025)

Linearizing Models for Efficient yet Robust Private Inference
por: Sarkar, Sreetama, et al.
Publicado: (2024)

Fingerprinting Deep Learning Models via Network Traffic Patterns in Federated Learning
por: Shuvo, Md Nahid Hasan, et al.
Publicado: (2025)

Data-adaptive Differentially Private Prompt Synthesis for In-Context Learning
por: Gao, Fengyu, et al.
Publicado: (2024)

Fed-EINI: An Efficient and Interpretable Inference Framework for Decision Tree Ensembles in Federated Learning
por: Chen, Xiaolin, et al.
Publicado: (2021)

VFLGAN: Vertical Federated Learning-based Generative Adversarial Network for Vertically Partitioned Data Publication
por: Yuan, Xun, et al.
Publicado: (2024)

Jailbreaking GPT-4V via Self-Adversarial Attacks with System Prompts
por: Wu, Yuanwei, et al.
Publicado: (2023)

Private-RAG: Answering Multiple Queries with LLMs while Keeping Your Data Private
por: Wu, Ruihan, et al.
Publicado: (2025)

OptiFLIDS: Optimized Federated Learning for Energy-Efficient Intrusion Detection in IoT
por: Elouardi, Saida, et al.
Publicado: (2025)

C2A: Client-Customized Adaptation for Parameter-Efficient Federated Learning
por: Kim, Yeachan, et al.
Publicado: (2024)

Hijack Vertical Federated Learning Models As One Party
por: Qiu, Pengyu, et al.
Publicado: (2022)

Communication-Efficient and Differentially Private Vertical Federated Learning with Zeroth-Order Optimization
por: Zhang, Jianing, et al.
Publicado: (2025)

Rounding-Guided Backdoor Injection in Deep Learning Model Quantization
por: Chen, Xiangxiang, et al.
Publicado: (2025)

EPSVec: Efficient and Private Synthetic Data Generation via Dataset Vectors
por: Banayeeanzade, Amin, et al.
Publicado: (2026)

FLClear: Visually Verifiable Multi-Client Watermarking for Federated Learning
por: Gu, Chen, et al.
Publicado: (2025)

Data Valuation and Detections in Federated Learning
por: Li, Wenqian, et al.
Publicado: (2023)

Oblivionis: A Lightweight Learning and Unlearning Framework for Federated Large Language Models
por: Zhang, Fuyao, et al.
Publicado: (2025)

Differentially Private Synthetic Data Generation Using Context-Aware GANs
por: Kotal, Anantaa, et al.
Publicado: (2025)