Under stringent privacy constraints, whether federated recommendation systems can achieve group fairness remains an inadequately explored question. Taking gender fairness as a representative issue, we identify three phenomena in federated recommendation systems: performance difference, data imbalance, and preference disparity. We discover that the state-of-the-art methods only focus on the first phenomenon. Consequently, their imposition of inappropriate fairness constraints detrimentally affects the model training. Moreover, due to insufficient sensitive attribute protection of existing works, we can infer the gender of all users with 99.90% accuracy even with the addition of maximal noise. In this work, we propose Privacy-Preserving Orthogonal Aggregation (PPOA), which employs the secure aggregation scheme and quantization technique, to prevent the suppression of minority groups by the majority and preserve the distinct preferences for better group fairness. PPOA can assist different groups in obtaining their respective model aggregation results through a designed orthogonal mapping while keeping their attributes private. Experimental results on three real-world datasets demonstrate that PPOA enhances recommendation effectiveness for both females and males by up to 8.25% and 6.36%, respectively, with a maximum overall improvement of 7.30%, and achieves optimal fairness in most cases. Extensive ablation experiments and visualizations indicate that PPOA successfully maintains preferences for different gender groups.

翻译：在严格的隐私约束下，联邦推荐系统能否实现群体公平性仍是一个尚未充分探索的问题。以性别公平性为代表议题，我们在联邦推荐系统中识别出三种现象：性能差异、数据不平衡和偏好差异。研究发现，现有最先进方法仅关注第一种现象，导致其施加的不当公平性约束对模型训练产生不利影响。此外，由于现有工作中敏感属性保护不足，即使添加最大噪声，我们仍能以99.90%的准确率推断所有用户的性别。本研究提出隐私保护正交聚合（PPOA），通过采用安全聚合方案和量化技术，防止多数群体对少数群体的压制，并保留差异化偏好以提升群体公平性。PPOA通过设计的正交映射，在保持属性私密性的同时，协助不同群体获取各自的模型聚合结果。在三个真实数据集上的实验结果表明，PPOA将女性和男性的推荐效果分别提升最高达8.25%和6.36%，整体最大提升达7.30%，并在多数情况下实现最优公平性。大量消融实验和可视化分析表明，PPOA成功保持了不同性别群体的偏好特征。