Federated Learning for the Design of Parametric Insurance Indices under Heterogeneous Renewable Production Losses
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arXiv
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866909993445359616 |
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| author | Niakh, Fallou |
| author_facet | Niakh, Fallou |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_12178 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Federated Learning for the Design of Parametric Insurance Indices under Heterogeneous Renewable Production Losses Niakh, Fallou Machine Learning 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. |
| title | Federated Learning for the Design of Parametric Insurance Indices under Heterogeneous Renewable Production Losses |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2601.12178 |