Federated Learning for Privacy-Preserving Financial Risk Forecasting Across Distributed Capital Markets
Keywords:
federated learning, privacy preservation, financial risk forecasting, capital markets, differential privacy, secure aggregation, distributed machine learning, financial regulation, adversarial robustness, governanceAbstract
The integration of machine learning into financial risk forecasting has historically required the aggregation of sensitive trading and portfolio data into centralized repositories, raising substantial privacy concerns and regulatory barriers. This paper proposes a federated learning framework that enables distributed capital markets to collaboratively train risk forecasting models without exposing raw client-level or institutional data. We examine the architectural trade-offs between model accuracy, communication efficiency, and statistical heterogeneity across participating entities. The study further analyzes how differential privacy mechanisms, secure aggregation protocols, and cryptographic primitives can be layered onto the federated pipeline to satisfy compliance with regulations such as the General Data Protection Regulation and the California Consumer Privacy Act. Governance challenges, including fair contribution metrics, incentive alignment, and auditability, are discussed in the context of multi-institutional consortia. Deployment sustainability concerns, such as energy consumption of iterative model updates and the robustness of the system to adversarial poisoning attacks, are evaluated through a synthesis of recent empirical results and theoretical guarantees. We also draw cross-domain comparisons from healthcare and mobile sensing applications to highlight structural parallels and transferable solutions. The paper concludes by outlining future research directions, including adaptive communication compression for latency-sensitive trading environments, verifiable computation for model integrity, and the incorporation of alternative data sources under privacy constraints. This work provides a comprehensive system-level roadmap for deploying federated learning in privacy-sensitive financial forecasting infrastructures.
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