Cross-silo federated learning allows multiple organizations to collaboratively train machine learning models without sharing raw data, but client updates can still leak sensitive information through inference attacks. Secure aggregation protects privacy by hiding individual updates, yet it complicates contribution evaluation, which is critical for fair rewards and detecting low-quality or malicious participants. Existing marginal-contribution methods, such as the Shapley value, are incompatible with secure aggregation, and practical alternatives, such as Leave-One-Out, are crude and rely on self-evaluation. We introduce two marginal-difference contribution scores compatible with secure aggregation. Fair-Private satisfies standard fairness axioms, while Everybody-Else eliminates self-evaluation and provides resistance to manipulation, addressing a largely overlooked vulnerability. We provide theoretical guarantees for fairness, privacy, robustness, and computational efficiency, and evaluate our methods on multiple medical image datasets and CIFAR10 in cross-silo settings. Our scores consistently outperform existing baselines, better approximate Shapley-induced client rankings, and improve downstream model performance as well as misbehavior detection. These results demonstrate that fairness, privacy, robustness, and practical utility can be achieved jointly in federated contribution evaluation, offering a principled solution for real-world cross-silo deployments.

翻译：跨机构联邦学习允许多个组织在不共享原始数据的情况下协作训练机器学习模型，但客户端更新仍可能通过推理攻击泄露敏感信息。安全聚合通过隐藏个体更新来保护隐私，但这使得贡献评估变得复杂，而贡献评估对于公平奖励以及检测低质量或恶意参与者至关重要。现有的边际贡献方法（如Shapley值）与安全聚合不兼容，而实用的替代方案（如留一法）则较为粗糙且依赖于自评估。我们提出了两种与安全聚合兼容的边际差异贡献评分方法。Fair-Private满足标准的公平性公理，而Everybody-Else则消除了自评估并提供了抗操纵能力，从而解决了一个长期被忽视的脆弱性问题。我们为公平性、隐私性、鲁棒性和计算效率提供了理论保证，并在跨机构设置下在多个医学影像数据集和CIFAR10上评估了我们的方法。我们的评分方法始终优于现有基线，能更好地近似Shapley值诱导的客户端排序，并提升下游模型性能以及不当行为检测能力。这些结果表明，在联邦贡献评估中，公平性、隐私性、鲁棒性和实际效用可以同时实现，为现实世界的跨机构部署提供了一个原则性的解决方案。