Image de couverture de livre

pFedFair: Towards Optimal Group Fairness-Accuracy Trade-off in Heterogeneous Federated Learning

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Lei, Haoyu, Gong, Shizhan, Dou, Qi, Farnia, Farzan
Format:	Preprint
Publié:	2025
Sujets:	Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

On the Inductive Biases of Demographic Parity-based Fair Learning Algorithms
par: Lei, Haoyu, et autres
Publié: (2024)

Structured Gradient-based Interpretations via Norm-Regularized Adversarial Training
par: Gong, Shizhan, et autres
Publié: (2024)

Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learning
par: Ye, Rongguang, et autres
Publié: (2025)

Controllable Pareto Trade-off between Fairness and Accuracy
par: Du, Yongkang, et autres
Publié: (2025)

Kernel-based Equalized Odds: A Quantification of Accuracy-Fairness Trade-off in Fair Representation Learning
par: Ni, Yijin, et autres
Publié: (2025)

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models
par: Gong, Shizhan, et autres
Publié: (2025)

A Super-pixel-based Approach to the Stable Interpretation of Neural Networks
par: Gong, Shizhan, et autres
Publié: (2024)

Gaussian Smoothing in Saliency Maps: The Stability-Fidelity Trade-Off in Neural Network Interpretability
par: Ye, Zhuorui, et autres
Publié: (2024)

You Only Debias Once: Towards Flexible Accuracy-Fairness Trade-offs at Inference Time
par: Han, Xiaotian, et autres
Publié: (2025)

Closing the Gap in the Trade-off between Fair Representations and Accuracy
par: Rout, Biswajit, et autres
Publié: (2024)

Certifiably Robust Model Evaluation in Federated Learning under Meta-Distributional Shifts
par: Najafi, Amir, et autres
Publié: (2024)

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

The Maximum von Neumann Entropy Principle: Theory and Applications in Machine Learning
par: Wu, Youqi, et autres
Publié: (2026)

FedShard: Federated Unlearning with Efficiency Fairness and Performance Fairness
par: Wen, Siyuan, et autres
Publié: (2025)

FairFedMed: Benchmarking Group Fairness in Federated Medical Imaging with FairLoRA
par: Li, Minghan, et autres
Publié: (2025)

Finite-Sample and Distribution-Free Fair Classification: Optimal Trade-off Between Excess Risk and Fairness, and the Cost of Group-Blindness
par: Hou, Xiaotian, et autres
Publié: (2024)

FedFACT: A Provable Framework for Controllable Group-Fairness Calibration in Federated Learning
par: Zhang, Li, et autres
Publié: (2025)

DAK-UCB: Diversity-Aware Prompt Routing for LLMs and Generative Models
par: Jafari, Donya, et autres
Publié: (2026)

Empirical Analysis of Asynchronous Federated Learning on Heterogeneous Devices: Efficiency, Fairness, and Privacy Trade-offs
par: Mohammadi, Samaneh, et autres
Publié: (2025)

SPARKE: Scalable Prompt-Aware Diversity and Novelty Guidance in Diffusion Models via RKE Score
par: Jalali, Mohammad, et autres
Publié: (2025)

Exploring Accuracy-Fairness Trade-off in Large Language Models
par: Zhang, Qingquan, et autres
Publié: (2024)

FedCF: Fair Federated Conformal Prediction
par: Srinivasan, Anutam, et autres
Publié: (2025)

FedFair^3: Unlocking Threefold Fairness in Federated Learning
par: Javaherian, Simin, et autres
Publié: (2024)

On the Fragility of AI-Based Channel Decoders under Small Channel Perturbations
par: Lei, Haoyu, et autres
Publié: (2026)

Be More Diverse than the Most Diverse: Optimal Mixtures of Generative Models via Mixture-UCB Bandit Algorithms
par: Rezaei, Parham, et autres
Publié: (2024)

Do Vendi Scores Converge with Finite Samples? Truncated Vendi Score for Finite-Sample Convergence Guarantees
par: Ospanov, Azim, et autres
Publié: (2024)

Certified Adversarial Robustness via Partition-based Randomized Smoothing
par: Goli, Hossein, et autres
Publié: (2024)

Boosting Cross-problem Generalization in Diffusion-Based Neural Combinatorial Solver via Inference Time Adaptation
par: Lei, Haoyu, et autres
Publié: (2025)

Trade-offs Between Individual and Group Fairness in Machine Learning: A Comprehensive Review
par: Benítez-Peña, Sandra, et autres
Publié: (2026)

The Fairness-Quality Trade-off in Clustering
par: Hakim, Rashida, et autres
Publié: (2024)

FedMABA: Towards Fair Federated Learning through Multi-Armed Bandits Allocation
par: Wang, Zhichao, et autres
Publié: (2024)

FedTilt: Towards Multi-Level Fairness-Preserving and Robust Federated Learning
par: Zhang, Binghui, et autres
Publié: (2025)

Edge AI in Highly Volatile Environments: Is Fairness Worth the Accuracy Trade-off?
par: Zaland, Obaidullah, et autres
Publié: (2025)

Sparse Domain Transfer via Elastic Net Regularization
par: Zhang, Jingwei, et autres
Publié: (2024)

Loss Gap Parity for Fairness in Heterogeneous Federated Learning
par: Erraji, Brahim, et autres
Publié: (2026)

An Information Theoretic Approach to Interaction-Grounded Learning
par: Hu, Xiaoyan, et autres
Publié: (2024)

Syndrome-Flow Consistency Model Achieves One-step Denoising Error Correction Codes
par: Lei, Haoyu, et autres
Publié: (2025)

Towards Collaborative Fairness in Federated Learning Under Imbalanced Covariate Shift
par: Yu, Tianrun, et autres
Publié: (2025)

Post-Fair Federated Learning: Achieving Group and Community Fairness in Federated Learning via Post-processing
par: Duan, Yuying, et autres
Publié: (2024)

PAK-UCB Contextual Bandit: An Online Learning Approach to Prompt-Aware Selection of Generative Models and LLMs
par: Hu, Xiaoyan, et autres
Publié: (2024)