We propose a federated learning framework for the calibration of parametric insurance indices under heterogeneous renewable energy production losses. Producers locally model their losses using Tweedie generalized linear models and private data, while a common index is learned through federated optimization without sharing raw observations. The approach accommodates heterogeneity in variance and link functions and directly minimizes a global deviance objective in a distributed setting. We implement and compare FedAvg, FedProx and FedOpt, and benchmark them against an existing approximation-based aggregation method. An empirical application to solar power production in Germany shows that federated learning recovers comparable index coefficients under moderate heterogeneity, while providing a more general and scalable framework.

翻译：本文提出了一种联邦学习框架，用于在异构可再生能源生产损失下校准参数化保险指数。生产者利用Tweedie广义线性模型和私有数据在本地建模其损失，同时通过联邦优化学习一个公共指数，而无需共享原始观测数据。该方法能够容纳方差和连接函数的异质性，并在分布式环境中直接最小化全局偏差目标。我们实现并比较了FedAvg、FedProx和FedOpt算法，并以现有的基于近似的聚合方法作为基准进行测试。针对德国太阳能发电的实证应用表明，在适度异质性条件下，联邦学习能够恢复可比较的指数系数，同时提供了一个更通用且可扩展的框架。