Hyperdimensional Federated Learning for DeFi Risk Mitigation and Portfolio Optimization

**Abstract:** This paper proposes a novel approach to decentralized finance (DeFi) risk mitigation and portfolio optimization leveraging hyperdimensional federated learning (HDFL). Conventional DeFi risk assessment models suffer from limited data accessibility and siloed information. Our system overcomes these challenges by securely aggregating portfolio performance data across decentralized nodes using hyperdimensional representations, enabling highly accurate and robust risk scoring combined with personalized portfolio optimization strategies. This approach promises a significant reduction in systemic risk within the DeFi ecosystem and allows for more effective resource allocation for individual investors.