Fairness in Federated Learning: Trends, Challenges, and Opportunities
Fuente:
arXiv
Guardado en:
| Autores principales: | Mukhtiar, Noorain, Mahmood, Adnan, Sheng, Quan Z. |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Fairness in Federated Learning: Trends, Challenges, and Opportunities
por: Noorain Mukhtiar, et al.
Publicado: (2025)
por: Noorain Mukhtiar, et al.
Publicado: (2025)
CoRe-Fed: Bridging Collaborative and Representation Fairness via Federated Embedding Distillation
por: Mukhtiar, Noorain, et al.
Publicado: (2026)
por: Mukhtiar, Noorain, et al.
Publicado: (2026)
Federated Learning at the Forefront of Fairness: A Multifaceted Perspective
por: Mukhtiar, Noorain, et al.
Publicado: (2026)
por: Mukhtiar, Noorain, et al.
Publicado: (2026)
FairEquityFL -- A Fair and Equitable Client Selection in Federated Learning for Heterogeneous IoV Networks
por: Islam, Fahmida, et al.
Publicado: (2025)
por: Islam, Fahmida, et al.
Publicado: (2025)
Post-Fair Federated Learning: Achieving Group and Community Fairness in Federated Learning via Post-processing
por: Duan, Yuying, et al.
Publicado: (2024)
por: Duan, Yuying, et al.
Publicado: (2024)
Fairness in Federated Learning: Fairness for Whom?
por: Taik, Afaf, et al.
Publicado: (2025)
por: Taik, Afaf, et al.
Publicado: (2025)
Semi-Variance Reduction for Fair Federated Learning
por: Malekmohammadi, Saber, et al.
Publicado: (2024)
por: Malekmohammadi, Saber, et al.
Publicado: (2024)
BoostFGL: Boosting Fairness in Federated Graph Learning
por: Chen, Zekai, et al.
Publicado: (2026)
por: Chen, Zekai, et al.
Publicado: (2026)
Distribution-Free Fair Federated Learning with Small Samples
por: Yin, Qichuan, et al.
Publicado: (2024)
por: Yin, Qichuan, et al.
Publicado: (2024)
Boosting Fairness and Robustness in Over-the-Air Federated Learning
por: Oksuz, Halil Yigit, et al.
Publicado: (2024)
por: Oksuz, Halil Yigit, et al.
Publicado: (2024)
FedCLF -- Towards Efficient Participant Selection for Federated Learning in Heterogeneous IoV Networks
por: Wijethilake, Kasun Eranda, et al.
Publicado: (2025)
por: Wijethilake, Kasun Eranda, et al.
Publicado: (2025)
Achieving Fairness Across Local and Global Models in Federated Learning
por: Makhija, Disha, et al.
Publicado: (2024)
por: Makhija, Disha, et al.
Publicado: (2024)
Fairness in Algorithmic Recourse Through the Lens of Substantive Equality of Opportunity
por: Bell, Andrew, et al.
Publicado: (2024)
por: Bell, Andrew, et al.
Publicado: (2024)
Training Fair Models in Federated Learning without Data Privacy Infringement
por: Che, Xin, et al.
Publicado: (2021)
por: Che, Xin, et al.
Publicado: (2021)
What Is the Point of Equality in Machine Learning Fairness? Beyond Equality of Opportunity
por: Kong, Youjin
Publicado: (2025)
por: Kong, Youjin
Publicado: (2025)
Friends in Unexpected Places: Enhancing Local Fairness in Federated Learning through Clustering
por: Yang, Yifan, et al.
Publicado: (2024)
por: Yang, Yifan, et al.
Publicado: (2024)
The Cost of Local and Global Fairness in Federated Learning
por: Duan, Yuying, et al.
Publicado: (2025)
por: Duan, Yuying, et al.
Publicado: (2025)
A Taxonomy of Challenges to Curating Fair Datasets
por: Zhao, Dora, et al.
Publicado: (2024)
por: Zhao, Dora, et al.
Publicado: (2024)
Open Datasets in Learning Analytics: Trends, Challenges, and Best PRACTICE
por: Švábenský, Valdemar, et al.
Publicado: (2026)
por: Švábenský, Valdemar, et al.
Publicado: (2026)
Fairness-aware Federated Minimax Optimization with Convergence Guarantee
por: Dunda, Gerry Windiarto Mohamad, et al.
Publicado: (2023)
por: Dunda, Gerry Windiarto Mohamad, et al.
Publicado: (2023)
What is Fair? Defining Fairness in Machine Learning for Health
por: Gao, Jianhui, et al.
Publicado: (2024)
por: Gao, Jianhui, et al.
Publicado: (2024)
A Post-Processing-Based Fair Federated Learning Framework
por: Zhou, Yi, et al.
Publicado: (2025)
por: Zhou, Yi, et al.
Publicado: (2025)
Procedural Fairness and Its Relationship with Distributive Fairness in Machine Learning
por: Wang, Ziming, et al.
Publicado: (2025)
por: Wang, Ziming, et al.
Publicado: (2025)
FedCD: A Fairness-aware Federated Cognitive Diagnosis Framework
por: Yang, Shangshang, et al.
Publicado: (2025)
por: Yang, Shangshang, et al.
Publicado: (2025)
AdapFair: Ensuring Adaptive Fairness for Machine Learning Operations
por: Huang, Yinghui, et al.
Publicado: (2024)
por: Huang, Yinghui, et al.
Publicado: (2024)
The Fragility of Fairness: Causal Sensitivity Analysis for Fair Machine Learning
por: Fawkes, Jake, et al.
Publicado: (2024)
por: Fawkes, Jake, et al.
Publicado: (2024)
FedFair^3: Unlocking Threefold Fairness in Federated Learning
por: Javaherian, Simin, et al.
Publicado: (2024)
por: Javaherian, Simin, et al.
Publicado: (2024)
The Age of Synthetic Realities: Challenges and Opportunities
por: Cardenuto, João Phillipe, et al.
Publicado: (2023)
por: Cardenuto, João Phillipe, et al.
Publicado: (2023)
Toward Fair Federated Learning under Demographic Disparities and Data Imbalance
por: Wu, Qiming, et al.
Publicado: (2025)
por: Wu, Qiming, et al.
Publicado: (2025)
GLOCALFAIR: Jointly Improving Global and Local Group Fairness in Federated Learning
por: Meerza, Syed Irfan Ali, et al.
Publicado: (2024)
por: Meerza, Syed Irfan Ali, et al.
Publicado: (2024)
Algorithmic Fairness in Performative Policy Learning: Escaping the Impossibility of Group Fairness
por: Somerstep, Seamus, et al.
Publicado: (2024)
por: Somerstep, Seamus, et al.
Publicado: (2024)
FairBranch: Mitigating Bias Transfer in Fair Multi-task Learning
por: Roy, Arjun, et al.
Publicado: (2023)
por: Roy, Arjun, et al.
Publicado: (2023)
Learning Interpretable Fair Representations
por: Wang, Tianhao, et al.
Publicado: (2024)
por: Wang, Tianhao, et al.
Publicado: (2024)
Fairness in Reinforcement Learning with Bisimulation Metrics
por: Rezaei-Shoshtari, Sahand, et al.
Publicado: (2024)
por: Rezaei-Shoshtari, Sahand, et al.
Publicado: (2024)
Normalise for Fairness: A Simple Normalisation Technique for Fairness in Regression Machine Learning Problems
por: Amin, Mostafa M., et al.
Publicado: (2022)
por: Amin, Mostafa M., et al.
Publicado: (2022)
FairFML: Fair Federated Machine Learning with a Case Study on Reducing Gender Disparities in Cardiac Arrest Outcome Prediction
por: Li, Siqi, et al.
Publicado: (2024)
por: Li, Siqi, et al.
Publicado: (2024)
PraFFL: A Preference-Aware Scheme in Fair Federated Learning
por: Ye, Rongguang, et al.
Publicado: (2024)
por: Ye, Rongguang, et al.
Publicado: (2024)
FairFedMed: Benchmarking Group Fairness in Federated Medical Imaging with FairLoRA
por: Li, Minghan, et al.
Publicado: (2025)
por: Li, Minghan, et al.
Publicado: (2025)
Deep Learning Model Reuse in the HuggingFace Community: Challenges, Benefit and Trends
por: Taraghi, Mina, et al.
Publicado: (2024)
por: Taraghi, Mina, et al.
Publicado: (2024)
Efficient k-means with Individual Fairness via Exponential Tilting
por: Zhu, Shengkun, et al.
Publicado: (2024)
por: Zhu, Shengkun, et al.
Publicado: (2024)
Ejemplares similares
-
Fairness in Federated Learning: Trends, Challenges, and Opportunities
por: Noorain Mukhtiar, et al.
Publicado: (2025) -
CoRe-Fed: Bridging Collaborative and Representation Fairness via Federated Embedding Distillation
por: Mukhtiar, Noorain, et al.
Publicado: (2026) -
Federated Learning at the Forefront of Fairness: A Multifaceted Perspective
por: Mukhtiar, Noorain, et al.
Publicado: (2026) -
FairEquityFL -- A Fair and Equitable Client Selection in Federated Learning for Heterogeneous IoV Networks
por: Islam, Fahmida, et al.
Publicado: (2025) -
Post-Fair Federated Learning: Achieving Group and Community Fairness in Federated Learning via Post-processing
por: Duan, Yuying, et al.
Publicado: (2024)