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This paper proposes a decentralized weather forecasting framework that integrates Federated Learning (FL) and blockchain technology to address the security vulnerabilities, scalability limitations, and single-point-of-failure issues of centralized weather forecasting systems. FL enables collaborative model training without exposing sensitive local data, thereby enhancing privacy and reducing data transmission overhead. The Ethereum blockchain ensures transparent and reliable verification of model updates. Furthermore, a reputation-based voting mechanism and IPFS are utilized to assess the reliability of submitted models and to implement efficient off-chain storage. Experimental results demonstrate that the proposed approach improves forecast accuracy and enhances the system's resilience and scalability, making it a viable candidate for deployment in real-world security-critical environments.
Takeaways, Limitations
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Takeaways:
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Presenting the possibility of solving the security and scalability issues of existing centralized systems through a distributed weather forecasting system.
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Strengthening privacy and increasing data efficiency through the combination of federated learning and blockchain technology.
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Enhanced system reliability and efficiency through reputation-based voting mechanisms and the use of IPFS.
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Improved forecast accuracy and system resilience and scalability.
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Limitations:
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Further analysis is needed on the cost and complexity of building and operating the proposed system in a real-world environment.
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Applicability and generalization performance evaluation for various meteorological data and forecast models are needed.
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Further verification of the fairness and durability of reputation-based voting mechanisms is needed.
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Consideration needs to be given to the scalability and transaction fee issues of the Ethereum blockchain.
