Weather forecasting plays a vital role in disaster preparedness, agriculture, and resource management, yet current centralized forecasting systems are increasingly strained by security vulnerabilities, limited scalability, and susceptibility to single points of failure. To address these challenges, we propose a decentralized weather forecasting framework that integrates Federated Learning (FL) with blockchain technology. FL enables collaborative model training without exposing sensitive local data; this approach enhances privacy and reduces data transfer overhead. Meanwhile, the Ethereum blockchain ensures transparent and dependable verification of model updates. To further enhance the system's security, we introduce a reputation-based voting mechanism that assesses the trustworthiness of submitted models while utilizing the Interplanetary File System (IPFS) for efficient off-chain storage. Experimental results demonstrate that our approach not only improves forecasting accuracy but also enhances system resilience and scalability, making it a viable candidate for deployment in real-world, security-critical environments.

翻译：天气预报在灾害预防、农业和资源管理中发挥着至关重要的作用，然而当前的中心化预报系统日益受到安全漏洞、有限的可扩展性以及单点故障风险的困扰。为应对这些挑战，我们提出了一种去中心化天气预报框架，该框架将联邦学习与区块链技术相结合。联邦学习能够在无需暴露敏感本地数据的情况下实现协同模型训练，从而增强隐私保护并降低数据传输开销。同时，以太坊区块链确保了模型更新的透明与可靠验证。为进一步提升系统安全性，我们引入了一种基于信誉的投票机制，用于评估所提交模型的可信度，并利用星际文件系统实现高效的链下存储。实验结果表明，我们的方法不仅提高了预报精度，还增强了系统的鲁棒性与可扩展性，使其成为可在现实世界中对安全性要求苛刻的环境中部署的可行方案。