Federated Model Updates

Architecture

Federated model updates function as a decentralized machine learning framework allowing crypto derivatives platforms to refine predictive pricing engines without centralizing sensitive client trade data. By distributing model training across edge nodes or local validator clusters, the system ensures raw order flow information remains private while contributing to global risk parameters. This structural design mitigates the hazards of data silos and enhances the robustness of volatility estimations across fragmented liquidity pools.