Federated learning (FL) faces challenges in uncertainty quantification (UQ). Without reliable UQ, FL systems risk deploying overconfident models at under-resourced agents, leading to silent local failures despite seemingly satisfactory global performance. Existing federated UQ approaches often address data heterogeneity or model heterogeneity in isolation, overlooking their joint effect on coverage reliability across agents. Conformal prediction is a widely used distribution-free UQ framework, yet its applications in heterogeneous FL settings remains underexplored. We provide FedWQ-CP, a simple yet effective approach that balances empirical coverage performance with efficiency at both global and agent levels under the dual heterogeneity. FedWQ-CP performs agent-server calibration in a single communication round. On each agent, conformity scores are computed on calibration data and a local quantile threshold is derived. Each agent then transmits only its quantile threshold and calibration sample size to the server. The server simply aggregates these thresholds through a weighted average to produce a global threshold. Experimental results on seven public datasets for both classification and regression demonstrate that FedWQ-CP empirically maintains agent-wise and global coverage while producing the smallest prediction sets or intervals.

翻译：联邦学习（FL）在不确定性量化（UQ）方面面临挑战。若缺乏可靠的UQ，联邦学习系统可能在资源受限的智能体上部署过度自信的模型，导致尽管全局性能看似令人满意，却出现局部静默故障。现有的联邦UQ方法通常单独处理数据异构性或模型异构性，忽视了二者对智能体间覆盖可靠性的联合影响。保形预测是一种广泛使用的无分布UQ框架，但其在异构联邦学习场景中的应用仍待深入探索。本文提出FedWQ-CP，一种在双重异构性下能同时平衡全局与智能体层面经验覆盖性能与效率的简洁有效方法。FedWQ-CP通过单轮通信完成智能体-服务器校准：各智能体基于校准数据计算符合度分数并推导本地分位数阈值，随后仅向服务器传输其分位数阈值与校准样本量；服务器通过加权平均聚合这些阈值以生成全局阈值。在七个公开分类与回归数据集上的实验结果表明，FedWQ-CP能在经验上保持智能体级与全局覆盖的同时，生成最小的预测集或预测区间。