Federated Multi-Agent Learning for Privacy-Preserving Financial Risk Management

Abstract

Financial risk management increasingly depends on distributed data, heterogeneous models, and rapid coordination across institutions, yet the most valuable risk signals often remain trapped inside organizational boundaries because of privacy, competition, and regulatory constraints. This paper examines federated multi-agent learning as a system-level architecture for privacy-preserving financial risk management. Rather than treating privacy as a post hoc compliance layer, the discussion positions federated coordination, multi-agent decision intelligence, and distributed governance as interdependent design commitments. The paper analyzes architectural foundations, including client heterogeneity, communication efficiency, secure aggregation, and trust calibration across autonomous agents. It then considers privacy and security risks such as inference attacks, poisoning, model leakage, and adversarial coordination, arguing that differential privacy and secure computation must be paired with institutional accountability and continuous monitoring. Governance receives sustained attention because cross-border financial supervision, auditability, liability allocation, and fairness cannot be resolved through technical protocols alone. The paper also evaluates deployment constraints, socio-technical integration, systemic risk, scalability, and sustainability. It suggests that federated multi-agent learning can improve risk detection and resilience when it is embedded in layered infrastructures that combine technical safeguards, regulatory oversight, participatory governance, and human sensemaking. However, the same architecture can amplify opacity, herding, exclusion, and supervisory blind spots if incentives and accountability are misaligned. The conclusion proposes research and policy directions for robust, fair, and sustainable federated financial intelligence.