Federated learning (FL) enables collaborative model training while preserving data privacy, making it suitable for decentralized human-centered AI applications. However, a significant research gap remains in ensuring fairness in these systems. Current fairness strategies in FL require knowledge of bias-creating/sensitive attributes, clashing with FL's privacy principles. Moreover, in human-centered datasets, sensitive attributes may remain latent. To tackle these challenges, we present a novel bias mitigation approach inspired by "Fairness without Demographics" in machine learning. The presented approach achieves fairness without needing knowledge of sensitive attributes by minimizing the top eigenvalue of the Hessian matrix during training, ensuring equitable loss landscapes across FL participants. Notably, we introduce a novel FL aggregation scheme that promotes participating models based on error rates and loss landscape curvature attributes, fostering fairness across the FL system. This work represents the first approach to attaining "Fairness without Demographics" in human-centered FL. Through comprehensive evaluation, our approach demonstrates effectiveness in balancing fairness and efficacy across various real-world applications, FL setups, and scenarios involving single and multiple bias-inducing factors, representing a significant advancement in human-centered FL.

翻译：联邦学习（FL）支持协同模型训练同时保护数据隐私，使其适用于去中心化的以人为中心的人工智能应用。然而，确保这些系统中的公平性仍存在显著的研究空白。当前FL中的公平性策略要求了解产生偏见/敏感的属性，这与FL的隐私原则相冲突。此外，在以人为中心的数据集中，敏感属性可能保持潜在状态。为应对这些挑战，我们提出了一种受机器学习中“无需人口统计信息的公平性”启发的新型偏见缓解方法。该方法通过训练过程中最小化Hessian矩阵的最大特征值，在无需了解敏感属性的情况下实现公平性，确保FL参与者之间损失景观的均等性。值得注意的是，我们引入了一种新颖的FL聚合方案，该方案根据错误率和损失景观曲率属性促进参与模型，从而增强整个FL系统的公平性。这项工作是首次在以人为中心的FL中实现“无需人口统计信息的公平性”。通过全面评估，我们的方法在平衡各种现实应用、FL设置以及涉及单因素和多因素偏见诱导场景中的公平性与有效性方面展现出效果，这代表了以人为中心FL领域的重大进展。