Article: Multimodal federated learning for detecting financial anomalies in enterprise cloud systems Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.34 pp.74 - 90 Abstract: Financial anomaly detection constitutes a critical pillar of corporate risk management. However, stringent data privacy regulations increasingly restrict centralised multimodal learning paradigms. To address this challenge, this paper proposed a novel multimodal federated learning framework deployed in a cloud environment. This system enables enterprises to process proprietary numerical and textual financial data locally. An attention-based cross-modal fusion module is introduced to effectively integrate heterogeneous features. Only model updates are transmitted to cloud servers for secure aggregation, thereby preserving data confidentiality. Furthermore, the framework incorporates adaptive client selection and differential privacy mechanisms to address non-IID data distributions and enhance security guarantees. Extensive evaluations on a real-world financial dataset demonstrate that our approach achieves 94.76% accuracy, 87.45% precision and 86.53% F1-score, substantially surpassing conventional unimodal and federated baselines while providing verifiable privacy assurances. This work presents a scalable and regulatory-compliant solution for collaborative financial risk intelligence. Inderscience Publishers - linking academia, business and industry through research

Title: Multimodal federated learning for detecting financial anomalies in enterprise cloud systems

Authors: Boyu Li

Addresses: Department of Economic Management, Hebei Chemical and Pharmaceutical College, Shijiazhuang, 050026, China

Abstract: Financial anomaly detection constitutes a critical pillar of corporate risk management. However, stringent data privacy regulations increasingly restrict centralised multimodal learning paradigms. To address this challenge, this paper proposed a novel multimodal federated learning framework deployed in a cloud environment. This system enables enterprises to process proprietary numerical and textual financial data locally. An attention-based cross-modal fusion module is introduced to effectively integrate heterogeneous features. Only model updates are transmitted to cloud servers for secure aggregation, thereby preserving data confidentiality. Furthermore, the framework incorporates adaptive client selection and differential privacy mechanisms to address non-IID data distributions and enhance security guarantees. Extensive evaluations on a real-world financial dataset demonstrate that our approach achieves 94.76% accuracy, 87.45% precision and 86.53% F1-score, substantially surpassing conventional unimodal and federated baselines while providing verifiable privacy assurances. This work presents a scalable and regulatory-compliant solution for collaborative financial risk intelligence.

Keywords: multimodal federated learning; financial anomaly detection; privacy protection; cloud-edge collaboration; attention mechanism.

DOI: 10.1504/IJICT.2026.152998

International Journal of Information and Communication Technology, 2026 Vol.27 No.34, pp.74 - 90

Received: 16 Dec 2025
Accepted: 19 Jan 2026
Published online: 17 Apr 2026 *

Title: Multimodal federated learning for detecting financial anomalies in enterprise cloud systems

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