Copertina

Improving LoRA in Privacy-preserving Federated Learning

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Sun, Youbang, Li, Zitao, Li, Yaliang, Ding, Bolin
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning Cryptography and Security Distributed, Parallel, and Cluster Computing
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

SHE-LoRA: Selective Homomorphic Encryption for Federated Tuning with Heterogeneous LoRA
di: Liu, Jianmin, et al.
Pubblicazione: (2025)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Federated Heterogeneous Graph Neural Network for Privacy-preserving Recommendation
di: Yan, Bo, et al.
Pubblicazione: (2023)

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

FedBiOT: LLM Local Fine-tuning in Federated Learning without Full Model
di: Wu, Feijie, et al.
Pubblicazione: (2024)

UNIDEAL: Curriculum Knowledge Distillation Federated Learning
di: Yang, Yuwen, et al.
Pubblicazione: (2023)

Foundation Models in Federated Learning: Assessing Backdoor Vulnerabilities
di: Li, Xi, et al.
Pubblicazione: (2024)

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

Hear No Evil: Detecting Gradient Leakage by Malicious Servers in Federated Learning
di: Wang, Fei, et al.
Pubblicazione: (2025)

Toward Malicious Clients Detection in Federated Learning
di: Dou, Zhihao, et al.
Pubblicazione: (2025)

PPFPL: Cross-silo Privacy-preserving Federated Prototype Learning Against Data Poisoning Attacks
di: Zhang, Hongliang, et al.
Pubblicazione: (2025)

Random Client Selection on Contrastive Federated Learning for Tabular Data
di: Ginanjar, Achmad, et al.
Pubblicazione: (2025)

Federated Multi-Task Learning on Non-IID Data Silos: An Experimental Study
di: Yang, Yuwen, et al.
Pubblicazione: (2024)

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

Advances in APPFL: A Comprehensive and Extensible Federated Learning Framework
di: Li, Zilinghan, et al.
Pubblicazione: (2024)

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning
di: Mo, Wenjin, et al.
Pubblicazione: (2025)

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

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

Privacy-preserving quantum federated learning via gradient hiding
di: Li, Changhao, et al.
Pubblicazione: (2023)

FedPDD: A Privacy-preserving Double Distillation Framework for Cross-silo Federated Recommendation
di: Wan, Sheng, et al.
Pubblicazione: (2023)

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

Communication-Efficient and Privacy-Preserving Decentralized Meta-Learning
di: Yang, Hansi, et al.
Pubblicazione: (2024)

Towards Trustworthy Federated Learning
di: Basharat, Alina, et al.
Pubblicazione: (2025)

LoRA-based Parameter-Efficient LLMs for Continuous Learning in Edge-based Malware Detection
di: Rondanini, Christian, et al.
Pubblicazione: (2026)