DFRA for Web3 Security: Leveraging Federated Learning and Decentralized Storage

Abstract

The increasing adoption of Decentralized Applications (DApps) and Web3 infrastructures has exposed critical security challenges, including malicious smart contracts, fraudulent transactions, and decentralized governance exploits. Traditional threat intelligence systems rely on centralized security models, which create single points of failure, reduce data sovereignty, and limit real-time risk mitigation. To address these challenges, we introduce a Decentralized Federated Risk Analysis (DFRA) system, leveraging federated risk aggregation, decentralized storage, and automated security intelligence retrieval to enhance cybersecurity in DApps. Our DFRA system operates through three primary components: (1) Federated Risk Aggregation, where a federated model retrieves and consolidates risk scores, flagged threats, and security insights across decentralized sources; (2) MinIOBased Decentralized Storage, which stores security intelligence in an object storage system to allow distributed retrieval; and (3) Automated Security Intelligence Retrieval, a server-based process that periodically fetches and processes security data in real-time.