International Journal on Science and Technology

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Context-Aware Federated Learning for Regulatory Risk Assessment in Financial Applications

Author(s) Sri Rama Chandra Charan Teja Tadi
Country United States
Abstract Federated learning facilitates model training scaling in distributed financial systems with data locality and regulatory compliance. Context-awareness integration increases model flexibility in terms of jurisdictional rules, transactional semantics, and user-level risk indicators. In the design of contemporary banking and finance applications, this integration can be facilitated by the modularity of services, secure APIs, and client-side execution patterns supportive of enterprise-class infrastructure. Context metadata, including time-stamped milestones, geographic compliance stamps, and activity signals, provides robustness to regional inference and facilitates global model convergence. Dynamic aggregation processes and adaptable participation mechanisms further enhance system flexibility and performance. The final product is an interpretive, privacy-aware regulatory risk assessment model deployable in institutionally segmented systems in real time.
Keywords Federated Learning, Context Awareness, Regulatory Compliance, Risk Assessment, Financial Systems, Client-Side Execution, Secure Aggregation, Distributed Modeling
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 15, Issue 4, October-December 2024
Published On 2024-12-05
Cite This Context-Aware Federated Learning for Regulatory Risk Assessment in Financial Applications - Sri Rama Chandra Charan Teja Tadi - IJSAT Volume 15, Issue 4, October-December 2024.
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