Cover Image

ALI-DPFL: Differentially Private Federated Learning with Adaptive Local Iterations

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Ling, Xinpeng, Fu, Jie, Wang, Kuncan, Liu, Haitao, Chen, Zhili
Format:	Preprint
Published:	2023
Subjects:	Machine Learning Cryptography and Security
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Differentially Private Federated Learning: A Systematic Review
by: Fu, Jie, et al.
Published: (2024)

FedFDP: Fairness-Aware Federated Learning with Differential Privacy
by: Ling, Xinpeng, et al.
Published: (2024)

EC-LDA : Label Distribution Inference Attack against Federated Graph Learning with Embedding Compression
by: Cheng, Tong, et al.
Published: (2025)

On Using Secure Aggregation in Differentially Private Federated Learning with Multiple Local Steps
by: Heikkilä, Mikko A.
Published: (2024)

Private and Communication-Efficient Federated Learning based on Differentially Private Sketches
by: Zhang, Meifan, et al.
Published: (2024)

Beyond Statistical Estimation: Differentially Private Individual Computation via Shuffling
by: Wang, Shaowei, et al.
Published: (2024)

Enabling Differentially Private Federated Learning for Speech Recognition: Benchmarks, Adaptive Optimizers and Gradient Clipping
by: Pelikan, Martin, et al.
Published: (2023)

Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation
by: Xu, Jie, et al.
Published: (2026)

Locally Differentially Private Distributed Online Learning with Guaranteed Optimality
by: Chen, Ziqin, et al.
Published: (2023)

Differentially Private Federated Learning without Noise Addition: When is it Possible?
by: Zhang, Jiang, et al.
Published: (2024)

Differentially Private Low-Rank Adaptation of Large Language Model Using Federated Learning
by: Liu, Xiao-Yang, et al.
Published: (2023)

Safeguarding Graph Neural Networks against Topology Inference Attacks
by: Fu, Jie, et al.
Published: (2025)

PrivORL: Differentially Private Synthetic Dataset for Offline Reinforcement Learning
by: Gong, Chen, et al.
Published: (2025)

On Evaluating the Poisoning Robustness of Federated Learning under Local Differential Privacy
by: Wang, Zijian, et al.
Published: (2025)

DPDR: Gradient Decomposition and Reconstruction for Differentially Private Deep Learning
by: Liu, Yixuan, et al.
Published: (2024)

Mitigating Noise Detriment in Differentially Private Federated Learning with Model Pre-training
by: Jin, Huitong, et al.
Published: (2024)

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy
by: You, Zhichao, et al.
Published: (2025)

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

Exactly Minimax-Optimal Locally Differentially Private Sampling
by: Park, Hyun-Young, et al.
Published: (2024)

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

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

The Last Iterate Advantage: Empirical Auditing and Principled Heuristic Analysis of Differentially Private SGD
by: Steinke, Thomas, et al.
Published: (2024)

An Iterative Algorithm for Differentially Private $k$-PCA with Adaptive Noise
by: Düngler, Johanna, et al.
Published: (2025)

Privately Learning Decision Lists and a Differentially Private Winnow
by: Bun, Mark, et al.
Published: (2026)

LanFL: Differentially Private Federated Learning with Large Language Models using Synthetic Samples
by: Wu, Huiyu, et al.
Published: (2024)

SPriFed-OMP: A Differentially Private Federated Learning Algorithm for Sparse Basis Recovery
by: Mulay, Ajinkya Kiran, et al.
Published: (2024)

Differentially Private Learned Indexes
by: Du, Jianzhang, et al.
Published: (2024)

Calibrating Practical Privacy Risks for Differentially Private Machine Learning
by: Gu, Yuechun, et al.
Published: (2024)

Differentially Private Iterative Screening Rules for Linear Regression
by: Khanna, Amol, et al.
Published: (2025)

Differentially Private Federated $k$-Means Clustering with Server-Side Data
by: Scott, Jonathan, et al.
Published: (2025)

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

Differential Private Federated Transfer Learning for Mental Health Monitoring in Everyday Settings: A Case Study on Stress Detection
by: Wang, Ziyu, et al.
Published: (2024)

Efficient Federated Unlearning with Adaptive Differential Privacy Preservation
by: Jiang, Yu, et al.
Published: (2024)

Data Poisoning Attacks to Locally Differentially Private Range Query Protocols
by: Liao, Ting-Wei, et al.
Published: (2025)

Locally Differentially Private Embedding Models in Distributed Fraud Prevention Systems
by: Perez, Iker, et al.
Published: (2024)

Correlated Noise Mechanisms for Differentially Private Learning
by: Pillutla, Krishna, et al.
Published: (2025)

Differentially Private Release and Learning of Threshold Functions
by: Bun, Mark, et al.
Published: (2015)

Differentially Private Decentralized Learning with Random Walks
by: Cyffers, Edwige, et al.
Published: (2024)

Delving into Differentially Private Transformer
by: Ding, Youlong, et al.
Published: (2024)

DMM: Distributed Matrix Mechanism for Differentially-Private Federated Learning Based on Constant-Overhead Linear Secret Resharing
by: Bienstock, Alexander, et al.
Published: (2024)