Fedfair: A Debiasing Algorithm for Federated Learning Systems

Federated learning has achieved remarkable results in a variety of decision-making tasks, but may create models that are biased toward some groups due to the labeling bias and distributional differences of client data. Therefore, exploring how to reduce decision-making bias is an urgent problem for future large-scale deployment of federated learning systems into society. The most effective approaches to enhance the fairness of federated learning include preprocessing, in-processing, and post-processing algorithms, which improve the fairness and credibility of the models while preserving privacy. However, existing debiasing algorithms are still limited in three aspects: poor fairness enhancement of the global model, high impact on the performance of the main task, and inconsistent evaluation metrics. Motivated by the challenge of group fairness in federated learning, we propose Fedfair, a debiasing algorithm for federated learning systems that differs from previous work in several key aspects: (1) it requires clients to upload a small amount of anonymized data to the server, which allows most centralized debiasing algorithms that rely on data to be applied to federated learning; (2) by mapping the implicit relationship between the model and the group bias, it fine-tunes the global model to reduce the group bias, thus weakening the impact on the model's performance on the main task; (3) it applies a unified fairness evaluation metric, which reasonably and intuitively demonstrates the advantages of debiasing compared to various other algorithms. Experiments verify that compared with other federated learning debiasing algorithms, Fedfair is more efficient, the debiasing effect is more obvious, and the impact on the performance of federated learning models is weaker.