Image de couverture de livre

OPUS-VFL: Incentivizing Optimal Privacy-Utility Tradeoffs in Vertical Federated Learning

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Madabushi, Sindhuja, Khan, Ahmad Faraz, Ali, Haider, Cho, Jin-Hee
Format:	Preprint
Publié:	2025
Sujets:	Machine Learning Artificial Intelligence
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

PRIVEE: Privacy-Preserving Vertical Federated Learning Against Feature Inference Attacks
par: Madabushi, Sindhuja, et autres
Publié: (2025)

MURIM: Multidimensional Reputation-based Incentive Mechanism for Federated Learning
par: Madabushi, Sindhuja, et autres
Publié: (2025)

IP-FL: Incentivized and Personalized Federated Learning
par: Khan, Ahmad Faraz, et autres
Publié: (2023)

Personalized Federated Learning Techniques: Empirical Analysis
par: Khan, Azal Ahmad, et autres
Publié: (2024)

Exploring the Privacy-Energy Consumption Tradeoff for Split Federated Learning
par: Lee, Joohyung, et autres
Publié: (2023)

PBM-VFL: Vertical Federated Learning with Feature and Sample Privacy
par: Tran, Linh, et autres
Publié: (2025)

Federated Learning with Differential Privacy: An Utility-Enhanced Approach
par: Ranaweera, Kanishka, et autres
Publié: (2025)

FLStore: Efficient Federated Learning Storage for non-training workloads
par: Khan, Ahmad Faraz, et autres
Publié: (2025)

VFL-RPS: Relevant Participant Selection in Vertical Federated Learning
par: Khan, Afsana, et autres
Publié: (2025)

Empirical Analysis of Privacy-Fairness-Accuracy Trade-offs in Federated Learning: A Step Towards Responsible AI
par: Wasif, Dawood, et autres
Publié: (2025)

Multi-Objective Optimization for Privacy-Utility Balance in Differentially Private Federated Learning
par: Ranaweera, Kanishka, et autres
Publié: (2025)

Modeling Multi-Objective Tradeoffs with Monotonic Utility Functions
par: Chen, Edward, et autres
Publié: (2024)

A Theoretical Analysis of Efficiency Constrained Utility-Privacy Bi-Objective Optimization in Federated Learning
par: Gu, Hanlin, et autres
Publié: (2023)

Data Valuation for Vertical Federated Learning: A Model-free and Privacy-preserving Method
par: Han, Xiao, et autres
Publié: (2021)

PackVFL: Efficient HE Packing for Vertical Federated Learning
par: Yang, Liu, et autres
Publié: (2024)

PUFFLE: Balancing Privacy, Utility, and Fairness in Federated Learning
par: Corbucci, Luca, et autres
Publié: (2024)

Federated Inference: Toward Privacy-Preserving Collaborative and Incentivized Model Serving
par: Seo, Jungwon, et autres
Publié: (2026)

Sem-DPO: Mitigating Semantic Inconsistency in Preference Optimization for Prompt Engineering
par: Mohamed, Anas, et autres
Publié: (2025)

The Impact of Generalization Techniques on the Interplay Among Privacy, Utility, and Fairness in Image Classification
par: Hassanpour, Ahmad, et autres
Publié: (2024)

A Unified Learn-to-Distort-Data Framework for Privacy-Utility Trade-off in Trustworthy Federated Learning
par: Zhang, Xiaojin, et autres
Publié: (2024)

Rethinking LoRA for Privacy-Preserving Federated Learning in Large Models
par: Liu, Jin, et autres
Publié: (2026)

LADs: Leveraging LLMs for AI-Driven DevOps
par: Khan, Ahmad Faraz, et autres
Publié: (2025)

FedPF: Accurate Target Privacy Preserving Federated Learning Balancing Fairness and Utility
par: Sun, Kangkang, et autres
Publié: (2025)

On the Tradeoffs of On-Device Generative Models in Federated Predictive Maintenance Systems
par: Milasheuski, Usevalad, et autres
Publié: (2026)

Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off
par: Li, Yuecheng, et autres
Publié: (2024)

Hybrid Federated and Split Learning for Privacy Preserving Clinical Prediction and Treatment Optimization
par: Akter, Farzana, et autres
Publié: (2026)

Assessing the Impact of Code Changes on the Fault Localizability of Large Language Models
par: Haroon, Sabaat, et autres
Publié: (2025)

fluke: Federated Learning Utility frameworK for Experimentation and research
par: Polato, Mirko
Publié: (2024)

Revisiting Privacy, Utility, and Efficiency Trade-offs when Fine-Tuning Large Language Models
par: Das, Soumi, et autres
Publié: (2025)

TabVFL: Improving Latent Representation in Vertical Federated Learning
par: Rashad, Mohamed, et autres
Publié: (2024)

Towards Achieving Near-optimal Utility for Privacy-Preserving Federated Learning via Data Generation and Parameter Distortion
par: Zhang, Xiaojin, et autres
Publié: (2023)

Vertical Federated Learning in Practice: The Good, the Bad, and the Ugly
par: Wu, Zhaomin, et autres
Publié: (2025)

Unlearning Clients, Features and Samples in Vertical Federated Learning
par: Varshney, Ayush K., et autres
Publié: (2025)

TreeCSS: An Efficient Framework for Vertical Federated Learning
par: Zhang, Qinbo, et autres
Publié: (2024)

On the Global Optimality of Policy Gradient Methods in General Utility Reinforcement Learning
par: Barakat, Anas, et autres
Publié: (2024)

Graph Federated Learning for Personalized Privacy Recommendation
par: Na, Ce, et autres
Publié: (2025)

A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspective
par: Yu, Lei, et autres
Publié: (2024)

Breaking the Safety-Capability Tradeoff: Reinforcement Learning with Verifiable Rewards Maintains Safety Guardrails in LLMs
par: Cho, Dongkyu Derek, et autres
Publié: (2025)

Tree-based Models for Vertical Federated Learning: A Survey
par: Qian, Bingchen, et autres
Publié: (2025)

Cumulative Utility Parity for Fair Federated Learning under Intermittent Client Participation
par: Behfar, Stefan, et autres
Publié: (2026)