Bias Mitigation in Federated Learning Through Feature Integration

Machine learning models are trained on datasets where undesired features have spurious correlations with the target variable, resulting in the bias shifts of the model output to a different class. In federated learning (FL), a common problem is data distribution skew. Since each client has a different distribution, models trained on these local datasets are likely to learn different biases. This phenomenon will degrade the performance of the aggregated global model and then prevent the global model from learning actual intrinsic decisions. Consequently, addressing and mitigating these biases in FL is critical to the overall effectiveness of the global model. Most existing works are designed for centralized machine learning using data augmentation or regularization terms. Nevertheless, these methods may not generalize effectively when directly migrated to federated learning scenarios due to the lack of direct access to the external data of other clients. Recent work for debiasing in FL is based on data augmentation in image space, which generates new bias-conflicting images for further training, leading to high costs and limited ability to create sufficiently diverse and realistic samples. To tackle these challenges, we propose a novel feature-level augmentation approach, named FedInt, for debiasing in federated learning. The feature-level augmentation in latent space allows the model to capture and smooth out different directions of the decision boundary, making it possible to synthesize diverse bias-conflicting samples for each client with lower training costs. Experiments on three datasets demonstrate the effectiveness of our proposed method in terms of debiasing performance, and it outperforms traditional data augmentation techniques and various typical FL methods. Our code is available on an anonymous GitHub repository: https://anonymous.4open.science/r/FedInt-C710889456/