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）支持在保护数据隐私的前提下进行协作模型训练，使其适用于去中心化的面向人类的人工智能应用。然而，确保这些系统的公平性仍存在显著的研究空白。当前FL中的公平性策略需知晓产生偏差/敏感属性，这与FL的隐私原则相冲突。此外，在面向人类的数据集中，敏感属性可能保持潜在状态。为应对这些挑战，我们提出了一种新颖的偏差缓解方法，其灵感来源于机器学习中的“无需人口统计信息的公平性”。该方法通过训练过程中最小化Hessian矩阵的最大特征值，无需知晓敏感属性即可实现公平性，从而确保FL参与者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，该方案基于错误率和损失景观曲率属性促进参与模型的演进，从而提升整个FL系统的公平性。本研究首次实现了面向人类FL中的“无需人口统计信息的公平性”。通过全面评估，我们的方法在平衡公平性与效能方面展现出有效性，适用于多种实际应用场景、FL设置以及涉及单因素和多因素偏差诱导的场景，标志着面向人类FL领域的重要进展。