Decentralized Weather Forecasting via Distributed Machine Learning and Blockchain-Based Model Validation

TL;DR

This paper presents a decentralized weather forecasting system combining federated learning and blockchain to improve privacy, security, scalability, and resilience over traditional centralized methods.

Contribution

It introduces a novel framework integrating federated learning with blockchain and IPFS, enhancing security, trust, and efficiency in distributed weather prediction.

Findings

Improved forecasting accuracy compared to centralized models.

Enhanced system resilience and scalability.

Secure and transparent model validation via blockchain.

Abstract

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.