用联邦学习与区块链实现更安全可靠的天气预报系统
Decentralized Weather Forecasting via Distributed Machine Learning and Blockchain-Based Model Validation
- 通过联邦学习实现多方协作训练，不共享原始数据
- 区块链确保模型更新可验证，准确率提升且系统更稳定
- 适合关注数据隐私与系统安全的气象与应急部门
天气预报在防灾、农业和资源管理中至关重要，但现有中心化系统面临安全漏洞、扩展性差和单点故障等问题。为此，我们提出一种融合联邦学习（FL）与区块链技术的去中心化天气预报框架。FL支持在不暴露敏感本地数据的前提下协同训练模型，提升隐私保护并降低数据传输开销；同时，基于以太坊的区块链实现模型更新的透明可信验证。为增强安全性，引入基于信誉的投票机制评估提交模型的可信度，并利用星际文件系统（IPFS）进行高效链外存储。实验表明，该方法不仅提升了预报准确率，还显著增强了系统的鲁棒性与可扩展性，适用于真实世界中对安全性要求高的场景。
原文摘要 · Abstract (English)
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.
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