Buchumschlag

Improving Parameter-Efficient Federated Learning with Differentially Private Refactorization

Fuente: arXiv

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Tran, Linh, Milanova, Ana, Patterson, Stacy
Format:	Preprint
Veröffentlicht:	2026
Schlagworte:	Cryptography and Security
Online-Zugang:	Acceder al recurso
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Ähnliche Einträge

A Differentially Private Blockchain-Based Approach for Vertical Federated Learning
von: Tran, Linh, et al.
Veröffentlicht: (2024)

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

Harnessing Sparsification in Federated Learning: A Secure, Efficient, and Differentially Private Realization
von: Xu, Shuangqing, et al.
Veröffentlicht: (2025)

Differentially Private Parameter-Efficient Fine-tuning for Large ASR Models
von: Liu, Hongbin, et al.
Veröffentlicht: (2024)

Enhancing Accuracy-Privacy Trade-off in Differentially Private Split Learning
von: Pham, Ngoc Duy, et al.
Veröffentlicht: (2023)

DP-BREM: Differentially-Private and Byzantine-Robust Federated Learning with Client Momentum
von: Gu, Xiaolan, et al.
Veröffentlicht: (2023)

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

Secure Stateful Aggregation: A Practical Protocol with Applications in Differentially-Private Federated Learning
von: Ball, Marshall, et al.
Veröffentlicht: (2024)

Bilateral Differentially Private Vertical Federated Boosted Decision Trees
von: Zhang, Bokang, et al.
Veröffentlicht: (2025)

PBM-VFL: Vertical Federated Learning with Feature and Sample Privacy
von: Tran, Linh, et al.
Veröffentlicht: (2025)

FederBoost: Private Federated Learning for GBDT
von: Tian, Zhihua, et al.
Veröffentlicht: (2020)

Advances in Differential Privacy and Differentially Private Machine Learning
von: Das, Saswat, et al.
Veröffentlicht: (2024)

Privacy-Preserving Personalized Federated Prompt Learning for Multimodal Large Language Models
von: Tran, Linh, et al.
Veröffentlicht: (2025)

Efficient Full-Stack Private Federated Deep Learning with Post-Quantum Security
von: Zhang, Yiwei, et al.
Veröffentlicht: (2025)

Oracle-Efficient Differentially Private Learning with Public Data
von: Block, Adam, et al.
Veröffentlicht: (2024)

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

ALI-DPFL: Differentially Private Federated Learning with Adaptive Local Iterations
von: Ling, Xinpeng, et al.
Veröffentlicht: (2023)

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

HeteroFedSyn: Differentially Private Tabular Data Synthesis for Heterogeneous Federated Settings
von: Li, Xiaochen, et al.
Veröffentlicht: (2026)

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

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

CryptPEFT: Efficient and Private Neural Network Inference via Parameter-Efficient Fine-Tuning
von: Xia, Saisai, et al.
Veröffentlicht: (2025)

DictPFL: Efficient and Private Federated Learning on Encrypted Gradients
von: Xue, Jiaqi, et al.
Veröffentlicht: (2025)

DP-SAPF: Saliency-Aware Parameter Fine-tuning of Public Models for Differentially Private Image Synthesis
von: Gong, Chen, et al.
Veröffentlicht: (2026)

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

DPDSyn: Improving Differentially Private Dataset Synthesis for Model Training by Downstream Task Guidance
von: Jia, Mingxuan, et al.
Veröffentlicht: (2026)

Cookie Monster: Efficient On-device Budgeting for Differentially-Private Ad-Measurement Systems
von: Tholoniat, Pierre, et al.
Veröffentlicht: (2024)

Q-ShiftDP: A Differentially Private Parameter-Shift Rule for Quantum Machine Learning
von: Ngo, Hoang M., et al.
Veröffentlicht: (2026)

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

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

MAUI: Reconstructing Private Client Data in Federated Transfer Learning
von: Dabholkar, Ahaan, et al.
Veröffentlicht: (2025)

Improving Count-Mean Sketch as the Leading Locally Differentially Private Frequency Estimator for Large Dictionaries
von: Pan, Mingen
Veröffentlicht: (2024)

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

Camel: Communication-Efficient and Maliciously Secure Federated Learning in the Shuffle Model of Differential Privacy
von: Xu, Shuangqing, et al.
Veröffentlicht: (2024)

Differentially Private Contrastive Learning via Bounding Group-level Contribution
von: Li, Kecen, et al.
Veröffentlicht: (2026)

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

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

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

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

Improved Differentially Private Algorithms for Rank Aggregation
von: Hillebrand, Quentin, et al.
Veröffentlicht: (2025)