Federated Learning Framework for Privacy-Preserving Credit Default Prediction under Dynamic Economic Regimes
Keywords:
federated learning, privacy preservation, credit default prediction, dynamic economic regimes, differential privacy, financial infrastructure, fairness, concept driftAbstract
This paper presents a comprehensive systems-level analysis of a federated learning framework designed for privacy-preserving credit default prediction under dynamic economic regimes. The proliferation of machine learning in financial risk assessment has raised significant concerns regarding data privacy, regulatory compliance, and model robustness, particularly when economic conditions shift unpredictably. We propose an architectural paradigm that integrates horizontal federated learning with differential privacy and secure aggregation to enable collaborative model training across financial institutions without exposing sensitive client data. The framework explicitly addresses the challenge of concept drift induced by changing macroeconomic environments by incorporating adaptive model recalibration mechanisms and regime detection modules. We examine structural trade-offs between privacy guarantees, model accuracy, communication efficiency, and computational overhead, drawing on cross-domain comparisons from healthcare and telecommunications. Governance considerations are emphasized, including fairness audits, bias mitigation, and accountability under evolving regulatory frameworks such as the General Data Protection Regulation and the Fair Credit Reporting Act. Infrastructure requirements for deployment at scale are discussed, including edge computing support, asynchronous updates, and energy sustainability. Through analytical discussion and illustrative case scenarios, we demonstrate that a carefully designed federated system can achieve robust, interpretable, and fair credit default predictions while preserving individual privacy. The paper concludes with forward-looking recommendations for policy design, standardization, and interdisciplinary research to operationalize privacy-preserving financial AI in volatile economic climates.
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