Federated Learning for the Design of Parametric Insurance Indices under Heterogeneous Renewable Production Losses

TL;DR

This paper introduces a federated learning framework for calibrating parametric insurance indices tailored to heterogeneous renewable energy losses, enabling privacy-preserving, scalable, and accurate modeling.

Contribution

It develops a novel federated optimization approach that handles heterogeneity in variance and link functions for renewable energy loss modeling.

Findings

Federated learning achieves comparable index coefficients under moderate heterogeneity.

The framework is more general and scalable than existing approximation-based methods.

Empirical results demonstrate effective calibration for solar power production data.

Abstract

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.