FAIR Federated Learning

FAIR Federated Learning (FAIR-FL) aims to train accurate and unbiased machine learning models collaboratively across decentralized datasets while preserving data privacy. Current research focuses on developing algorithms that mitigate performance disparities among participating clients, often employing techniques like variance reduction, adaptive aggregation weights, and bilevel optimization to achieve fairness goals such as minimizing group loss discrepancies or maximizing minimum client accuracy. This field is significant because it addresses critical ethical and practical concerns in distributed machine learning, enabling the development of more equitable and robust AI systems across diverse applications, particularly in sensitive domains like healthcare and finance.

Papers

November 12, 2024

A Stochastic Optimization Framework for Private and Fair Learning From Decentralized Data
Devansh Gupta, A.S. Poornash, Andrew Lowy, Meisam Razaviyayn
Differential Privacy Decentralized Data Fairness Notion Fairness Accuracy FAIR Federated Learning Fair Learning

October 26, 2024

FedMABA: Towards Fair Federated Learning through Multi-Armed Bandits Allocation
Zhichao Wang, Lin Wang, Yongxin Guo, Ying-Jun Angela Zhang, Xiaoying Tang
Multi Armed Bandit Privacy Preserving Capital Allocation FAIR Federated Learning Federated Bandit Adversarial Multi Armed Bandit

October 7, 2024

FairFML: Fair Federated Machine Learning with a Case Study on Reducing Gender Disparities in Cardiac Arrest Outcome Prediction
Siqi Li, Qiming Wu, Xin Li, Di Miao, Chuan Hong, Wenjun Gu, Yuqing Shang, Yohei Okada, Michael Hao Chen, Mengying Yan, Yilin Ning, Marcus Eng Hock Ong, Nan Liu
Case Study Model Fairness FAIR Federated Learning Stroke Prediction Gender Inequality

August 21, 2024

RFID based Health Adherence Medicine Case Using Fair Federated Learning
Ali Kamrani khodaei, Sina Hajer Ahmadi
Smart Healthcare FAIR Federated Learning Guideline Adherence Intelligent Healthcare

June 23, 2024

Semi-Variance Reduction for Fair Federated Learning
Saber Malekmohammadi
Loss Function Variance Reduction FAIR Federated Learning Fair Federated Learning

May 26, 2024

Fair Federated Learning under Domain Skew with Local Consistency and Domain Diversity
Yuhang Chen, Wenke Huang, Mang Ye
Model Bias FAIR Federated Learning Domain Diversity

April 27, 2024

From Optimization to Generalization: Fair Federated Learning against Quality Shift via Inter-Client Sharpness Matching
Nannan Wu, Zhuo Kuang, Zengqiang Yan, Li Yu
Strong Generalization Optimization Purpose Image Quality Quality Issue Better Generalization FAIR Federated Learning Sharpness Measure

April 13, 2024

PraFFL: A Preference-Aware Scheme in Fair Federated Learning
Rongguang Ye, Wei-Bin Kou, Ming Tang
Procedural Fairness FAIR Federated Learning Interest Preference

February 25, 2024

Distribution-Free Fair Federated Learning with Small Samples
Qichuan Yin, Zexian Wang, Junzhou Huang, Huaxiu Yao, Linjun Zhang
Model Training Fairness Aware Machine Distribution Free Client Heterogeneity Single Sample FAIR Federated Learning

February 8, 2024

Reinforcement Learning as a Catalyst for Robust and Fair Federated Learning: Deciphering the Dynamics of Client Contributions
Jialuo He, Wei Chen, Xiaojin Zhang
Reinforcement Learning Adversarial Attack Deep Reinforcement Learning Native Robustness Robust Version Client Contribution Federated Reinforcement Learning FAIR Federated Learning

January 10, 2024

AdaFed: Fair Federated Learning via Adaptive Common Descent Direction
Shayan Mohajer Hamidi, En-Hui Yang
Loss Function Local Gradient FAIR Federated Learning Gradient Sharing

December 9, 2023

Multi-dimensional Fair Federated Learning
Cong Su, Guoxian Yu, Jun Wang, Hui Li, Qingzhong Li, Han Yu
Procedural Fairness Group Fairness Fairness Constraint FAIR Federated Learning Proportional Fairness

November 2, 2023

Dynamic Fair Federated Learning Based on Reinforcement Learning
Weikang Chen, Junping Du, Yingxia Shao, Jia Wang, Yangxi Zhou
Reinforcement Learning Procedural Fairness FAIR Federated Learning Dynamic Fairness

September 28, 2023

Anti-Matthew FL: Bridging the Performance Gap in Federated Learning to Counteract the Matthew Effect
Jiashi Gao, Xin Yao, Xuetao Wei
Biased Decision FAIR Federated Learning Lombard Effect Egalitarian Fairness

July 18, 2023

Explanation-Guided Fair Federated Learning for Transparent 6G RAN Slicing
Swastika Roy, Hatim Chergui, Christos Verikoukis
Artificial Intelligence Explainable AI XAI Method Transparency Index Faithfulness Test FAIR Federated Learning Zero Touch

May 4, 2023

Can Fair Federated Learning reduce the need for Personalisation?
Alex Iacob, Pedro P. B. Gusmão, Nicholas D. Lane
Federated Learning Community Need Federated Setting Personalization Performance FAIR Federated Learning Local Client Model

January 29, 2023

FedEBA+: Towards Fair and Effective Federated Learning via Entropy-Based Model
Lin Wang, Zhichao Wang, Sai Praneeth Karimireddy, Xiaoying Tang
Federated Learning Entropy Model Achieving Fairness FAIR Federated Learning Fairness Aware Aggregation

January 23, 2023

Accelerating Fair Federated Learning: Adaptive Federated Adam
Li Ju, Tianru Zhang, Salman Toor, Andreas Hellander
FAIR Federated Learning Fairness Property Adaptive Federated Learning

October 22, 2022

On-Demand Sampling: Learning Optimally from Multiple Distributions
Nika Haghtalab, Michael I. Jordan, Eric Zhao
Sample Complexity Sample Complexity Bound FAIR Federated Learning Multiple Distribution Multi Distribution

September 27, 2022

FAIR-FATE: Fair Federated Learning with Momentum
Teresa Salazar, Miguel Fernandes, Helder Araujo, Pedro Henriques Abreu
Residual Momentum Fairness Aware Machine FAIR Federated Learning Fairness Aware Aggregation Fair Federated Learning