Buchumschlag

Adaptive Differential Privacy in Federated Learning: A Priority-Based Approach

Fuente: arXiv

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Talaei, Mahtab, Izadi, Iman
Format:	Preprint
Veröffentlicht:	2024
Schlagworte:	Cryptography and Security Distributed, Parallel, and Cluster Computing Machine Learning
Online-Zugang:	Acceder al recurso
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Ähnliche Einträge

Enhancing Federated Learning with Adaptive Differential Privacy and Priority-Based Aggregation
von: Talaei, Mahtab, et al.
Veröffentlicht: (2024)

Comments on "Federated Learning with Differential Privacy: Algorithms and Performance Analysis"
von: Talaei, Mahtab, et al.
Veröffentlicht: (2024)

Differentially Private Federated Learning With Time-Adaptive Privacy Spending
von: Kiani, Shahrzad, et al.
Veröffentlicht: (2025)

The Effect of Quantization in Federated Learning: A Rényi Differential Privacy Perspective
von: Kang, Tianqu, et al.
Veröffentlicht: (2024)

A Lightweight Federated Learning Approach for Privacy-Preserving Botnet Detection in IoT
von: Mahmoud, Taha M., et al.
Veröffentlicht: (2025)

Enhancing Privacy in Decentralized Min-Max Optimization: A Differentially Private Approach
von: Quan, Yueyang, et al.
Veröffentlicht: (2025)

Differentially Private Clustered Federated Learning
von: Malekmohammadi, Saber, et al.
Veröffentlicht: (2024)

No Vandalism: Privacy-Preserving and Byzantine-Robust Federated Learning
von: Xing, Zhibo, et al.
Veröffentlicht: (2024)

Improving LoRA in Privacy-preserving Federated Learning
von: Sun, Youbang, et al.
Veröffentlicht: (2024)

Differentially-Private Multi-Tier Federated Learning
von: Chen, Evan, et al.
Veröffentlicht: (2024)

Share Secrets for Privacy: Confidential Forecasting with Vertical Federated Learning
von: Shankar, Aditya, et al.
Veröffentlicht: (2024)

Practical Framework for Privacy-Preserving and Byzantine-robust Federated Learning
von: Zhang, Baolei, et al.
Veröffentlicht: (2025)

IntraShuffler: A Privacy Preserving Framework for Heterogeneous DP Federated Learning
von: Riya, Farhin Farhad, et al.
Veröffentlicht: (2026)

A Framework for Evaluating Privacy-Utility Trade-off in Vertical Federated Learning
von: Kang, Yan, et al.
Veröffentlicht: (2022)

Privacy Preserving Machine Learning Model Personalization through Federated Personalized Learning
von: Hosain, Md. Tanzib, et al.
Veröffentlicht: (2025)

Age Aware Scheduling for Differentially-Private Federated Learning
von: Lin, Kuan-Yu, et al.
Veröffentlicht: (2024)

Differentially Private Online Federated Learning with Correlated Noise
von: Zhang, Jiaojiao, et al.
Veröffentlicht: (2024)

Brave: Byzantine-Resilient and Privacy-Preserving Peer-to-Peer Federated Learning
von: Xu, Zhangchen, et al.
Veröffentlicht: (2024)

Securing Health Data on the Blockchain: A Differential Privacy and Federated Learning Framework
von: Commey, Daniel, et al.
Veröffentlicht: (2024)

Noise-Aware Algorithm for Heterogeneous Differentially Private Federated Learning
von: Malekmohammadi, Saber, et al.
Veröffentlicht: (2024)

Efficient Language Model Architectures for Differentially Private Federated Learning
von: Ro, Jae Hun, et al.
Veröffentlicht: (2024)

Linkage on Security, Privacy and Fairness in Federated Learning: New Balances and New Perspectives
von: Wang, Linlin, et al.
Veröffentlicht: (2024)

DPBalance: Efficient and Fair Privacy Budget Scheduling for Federated Learning as a Service
von: Liu, Yu, et al.
Veröffentlicht: (2024)

Detect \& Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning
von: Xhemrishi, Marvin, et al.
Veröffentlicht: (2025)

An Adaptive Differential Privacy Method Based on Federated Learning
von: Wang, Zhiqiang, et al.
Veröffentlicht: (2024)

Federated Graph Learning with Adaptive Importance-based Sampling
von: Li, Anran, et al.
Veröffentlicht: (2024)

TimberStrike: Dataset Reconstruction Attack Revealing Privacy Leakage in Federated Tree-Based Systems
von: Di Gennaro, Marco, et al.
Veröffentlicht: (2025)

Deep Transfer Hashing for Adaptive Learning on Federated Streaming Data
von: Röder, Manuel, et al.
Veröffentlicht: (2024)

Lightweight Federated Learning with Differential Privacy and Straggler Resilience
von: Hong, Shu, et al.
Veröffentlicht: (2024)

zkFL-Health: Blockchain-Enabled Zero-Knowledge Federated Learning for Medical AI Privacy
von: Sharma, Savvy, et al.
Veröffentlicht: (2025)

FLea: Addressing Data Scarcity and Label Skew in Federated Learning via Privacy-preserving Feature Augmentation
von: Xia, Tong, et al.
Veröffentlicht: (2023)

Differential Privacy Analysis of Decentralized Gossip Averaging under Varying Threat Models
von: Koskela, Antti, et al.
Veröffentlicht: (2025)

Achieving Byzantine-Resilient Federated Learning via Layer-Adaptive Sparsified Model Aggregation
von: Xu, Jiahao, et al.
Veröffentlicht: (2024)

Defending Against Data Reconstruction Attacks in Federated Learning: An Information Theory Approach
von: Tan, Qi, et al.
Veröffentlicht: (2024)

DP-DyLoRA: Fine-Tuning Transformer-Based Models On-Device under Differentially Private Federated Learning using Dynamic Low-Rank Adaptation
von: Xu, Jie, et al.
Veröffentlicht: (2024)

DP$^2$-FedSAM: Enhancing Differentially Private Federated Learning Through Personalized Sharpness-Aware Minimization
von: Zhang, Zhenxiao, et al.
Veröffentlicht: (2024)

FedTGP: Trainable Global Prototypes with Adaptive-Margin-Enhanced Contrastive Learning for Data and Model Heterogeneity in Federated Learning
von: Zhang, Jianqing, et al.
Veröffentlicht: (2024)

A Survey on Decentralized Federated Learning
von: Gabrielli, Edoardo, et al.
Veröffentlicht: (2023)

Protecting Confidentiality, Privacy and Integrity in Collaborative Learning
von: Chen, Dong, et al.
Veröffentlicht: (2024)

Balancing Privacy, Robustness, and Efficiency in Machine Learning
von: Allouah, Youssef, et al.
Veröffentlicht: (2023)