Addressing Bias and Fairness Using Fair Federated Learning: A Synthetic Review

The rapid increase in data volume and variety within the field of machine learning necessitates ethical data utilization and adherence to strict privacy protection standards. Fair federated learning (FFL) has emerged as a pivotal solution for ensuring fairness and privacy protection within distributed learning environments. FFL not only enhances privacy safeguards but also addresses inherent limitations of existing federated learning (FL) systems by fostering equitable model training across diverse participant groups, mitigating the exclusion of individual users or minorities, and improving overall model fairness. This study examines the causes of bias and fairness within existing FL systems and categorizes solutions according to data partitioning strategies, privacy mechanisms, applicable machine learning models, communication architectures, and technologies designed to manage heterogeneity. To mitigate bias, enhance fairness, and strengthen privacy protections in FL, this study also explores fairness evaluation metrics, relevant applications, and associated challenges of FFL. Addressing bias, fairness, and privacy concerns across all mechanisms serves as a valuable resource for practitioners aiming to develop efficient FL solutions.