Cover Image

A Locally Differential Private Coding-Assisted Succinct Histogram Protocol

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Liu, Hsuan-Po, Mahdavifar, Hessam
Format:	Preprint
Published:	2025
Subjects:	Cryptography and Security Distributed, Parallel, and Cluster Computing Machine Learning Signal Processing
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

TernaryVote: Differentially Private, Communication Efficient, and Byzantine Resilient Distributed Optimization on Heterogeneous Data
by: Jin, Richeng, et al.
Published: (2024)

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

Resilience in Online Federated Learning: Mitigating Model-Poisoning Attacks via Partial Sharing
by: Lari, Ehsan, et al.
Published: (2024)

Privacy-Preserving Distributed Nonnegative Matrix Factorization
by: Lari, Ehsan, et al.
Published: (2024)

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

SENTINEL: Securing Indoor Localization against Adversarial Attacks with Capsule Neural Networks
by: Gufran, Danish, et al.
Published: (2024)

Differentially Private Clustered Federated Learning
by: Malekmohammadi, Saber, et al.
Published: (2024)

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

On the Potential of Re-configurable Intelligent Surface (RIS)-assisted Physical Layer Authentication (PLA)
by: Amin, Hala, et al.
Published: (2024)

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

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

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

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

Dropout-Robust Mechanisms for Differentially Private and Fully Decentralized Mean Estimation
by: Sabater, César, et al.
Published: (2025)

Efficient and Scalable Implementation of Differentially Private Deep Learning without Shortcuts
by: Beltran, Sebastian Rodriguez, et al.
Published: (2024)

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

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

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

Private Aggregation in Hierarchical Wireless Federated Learning with Partial and Full Collusion
by: Egger, Maximilian, et al.
Published: (2023)

Prada: Black-Box LLM Adaptation with Private Data on Resource-Constrained Devices
by: Wang, Ziyao, et al.
Published: (2025)

Inclusive, Differentially Private Federated Learning for Clinical Data
by: Parampottupadam, Santhosh, et al.
Published: (2025)

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

Adaptive Differential Privacy in Federated Learning: A Priority-Based Approach
by: Talaei, Mahtab, et al.
Published: (2024)

Differentially Private Secure Multiplication: Hiding Information in the Rubble of Noise
by: Cadambe, Viveck R., et al.
Published: (2023)

Effect of Full Common Randomness Replication in Symmetric PIR on Graph-Based Replicated Systems
by: Meel, Shreya, et al.
Published: (2025)

Privacy-Preserving Distributed Maximum Consensus Without Accuracy Loss
by: Yu, Wenrui, et al.
Published: (2024)

Aggressive or Imperceptible, or Both: Network Pruning Assisted Hybrid Byzantines in Federated Learning
by: Ozfatura, Emre, et al.
Published: (2024)

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

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

Federated Survival Analysis with Node-Level Differential Privacy: Private Kaplan-Meier Curves
by: Veeraragavan, Narasimha Raghavan, et al.
Published: (2025)

Adaptive Verifiable Coded Computing: Towards Fast, Secure and Private Distributed Machine Learning
by: Tang, Tingting, et al.
Published: (2021)

DP-LAC: Lightweight Adaptive Clipping for Differentially Private Federated Fine-tuning of Language Models
by: Mehmood, Haaris, et al.
Published: (2026)

Adversarial Analysis of the Differentially-Private Federated Learning in Cyber-Physical Critical Infrastructures
by: Hossain, Md Tamjid, et al.
Published: (2022)

A Survey for Federated Learning Evaluations: Goals and Measures
by: Chai, Di, et al.
Published: (2023)

A Whole-Process Certifiably Robust Aggregation Method Against Backdoor Attacks in Federated Learning
by: Zhou, Anqi, et al.
Published: (2024)

Private Model Personalization Revisited
by: Snedeker, Conor, et al.
Published: (2025)

Poisoning Attacks and Defenses to Federated Unlearning
by: Wang, Wenbin, et al.
Published: (2025)

Private Knowledge Sharing in Distributed Learning: A Survey
by: Supeksala, Yasas, et al.
Published: (2024)

Byzantine-Robust Decentralized Federated Learning
by: Fang, Minghong, et al.
Published: (2024)

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