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

2025-08-12

AI Summary

Centralized weather forecasting systems suffer from security vulnerabilities, single-point-of-failure risks, and scalability bottlenecks. To address these challenges, this paper proposes a decentralized weather forecasting framework integrating federated learning and blockchain. The framework enables privacy-preserving, multi-node collaborative modeling; introduces a reputation-based on-chain voting mechanism to validate model updates, thereby enhancing consensus security; and leverages IPFS for efficient, tamper-resistant off-chain storage of models and data. Experimental results demonstrate that the proposed approach significantly improves system resilience, fault tolerance, and horizontal scalability—without compromising prediction accuracy—while establishing a verifiable and auditable trust infrastructure. This work contributes a novel paradigm for trustworthy distributed AI in critical infrastructure domains.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Multiagent Systems: Other Foundations of Multi Agent Systems
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Decentralized Web and Fediverse systems
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Security and Privacy: Blockchains and distributed ledgers

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.

Problem

• Decentralized weather forecasting to address security and scalability issues
• Integrating Federated Learning with blockchain for privacy and model validation
• Enhancing system resilience and accuracy via reputation-based mechanisms

Innovation

• Federated Learning for privacy-preserving model training
• Blockchain for transparent model validation
• IPFS for efficient off-chain storage