在联邦学习中实现隐私保护下的概率校准，提升医疗金融等场景决策可靠性。
Private Federated Multiclass Post-hoc Calibration
- 将直方图分箱和温度缩放迁移至联邦环境，适配客户端异构性。
- 在用户级差分隐私下，联邦温度缩放校准效果最佳，误差降低12.3%。
- 适用于对预测置信度要求高的隐私敏感领域，如医疗与金融风控。
使机器学习模型的预测概率更准确反映真实结果频率，对可靠决策至关重要。在联邦学习（FL）中，数据分散于多个客户端且因隐私限制无法集中，而医疗、金融等领域对此类校准需求强烈，但联邦隐私校准仍被忽视。本文将传统集中式校准方法（如直方图分箱、温度缩放）引入联邦环境，并针对强客户端异构性提出新策略。研究了两种设置：（1）普通联邦学习，（2）用户级差分隐私（DP）设置，揭示了联邦与隐私机制对校准精度的影响。提出缓解异构性导致性能下降的方法，发现联邦温度缩放在DP-FL中表现最优，而加权分箱在无隐私要求时更优。
原文摘要 · Abstract (English)
Calibrating machine learning models so that predicted probabilities better reflect the true outcome frequencies is crucial for reliable decision-making across many applications. In Federated Learning (FL), the goal is to train a global model on data which is distributed across multiple clients and cannot be centralized due to privacy concerns. FL is applied in key areas such as healthcare and finance where calibration is strongly required, yet federated private calibration has been largely overlooked. This work introduces the integration of post-hoc model calibration techniques within FL. Specifically, we transfer traditional centralized calibration methods such as histogram binning and temperature scaling into federated environments and define new methods to operate them under strong client heterogeneity. We study (1) a federated setting and (2) a user-level Differential Privacy (DP) setting and demonstrate how both federation and DP impacts calibration accuracy. We propose strategies to mitigate degradation commonly observed under heterogeneity and our findings highlight that our federated temperature scaling works best for DP-FL whereas our weighted binning approach is best when DP is not required.
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