Decentralized Weather Forecasting via Distributed Machine Learning and Blockchain-Based Model Validation
This addresses security and scalability issues in weather forecasting for disaster preparedness and agriculture, though it is incremental as it combines existing technologies.
The paper tackles the limitations of centralized weather forecasting systems by proposing a decentralized framework that integrates Federated Learning with blockchain technology, resulting in improved forecasting accuracy, resilience, and scalability for real-world deployment.
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.