mlsanityData to 14 Oct 2026

Integrating Feature Attention and Temporal Modeling for Collaborative Financial Risk Assessment

arXiv 2508.09399 ↗First posted 13 Aug 2025cs.LG cs.CRcited by 3

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Abstract (arXiv)

This paper addresses the challenges of data privacy and collaborative modeling in cross-institution financial risk analysis. It proposes a risk assessment framework based on federated learning. Without sharing raw data, the method enables joint modeling and risk identification across multiple institutions. This is achieved by incorporating a feature attention mechanism and temporal modeling structure. Specifically, the model adopts a distributed optimization strategy. Each financial institution trains a local sub-model. The model parameters are protected using differential privacy and noise injection before being uploaded. A central server then aggregates these parameters to generate a global model. This global model is used for systemic risk identification. To validate the effectiveness of the proposed method, multiple experiments are conducted. These evaluate communication efficiency, model accuracy, systemic risk detection, and cross-market generalization. The results show that the proposed model outperforms both traditional centralized methods and existing federated learning variants across all evaluation metrics. It demonstrates strong modeling capabilities and practical value in sensitive financial environments. The method enhances the scope and efficiency of risk identification while preserving data sovereignty. It offers a secure and efficient solution for intelligent financial risk analysis.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 12 of its 43 references that are in the map. First posted on arXiv 13 Aug 2025.

Builds on

12 of 43 references.

References · 12

  1. 2025Collaborative Multi-Agent Reinforcement Learning Approach for Elastic Cloud Resource Scalingcited by 10
  2. 2025Addressing Class Imbalance with Probabilistic Graphical Models and Variational Inferencecited by 9
  3. 2025Detecting Credit Card Fraud via Heterogeneous Graph Neural Networks with Graph Attentioncited by 8
  4. 2025A Deep Learning Framework for Sequence Mining with Bidirectional LSTM and Multi-Scale Attentioncited by 7
  5. 2025Joint Graph Convolution and Sequential Modeling for Scalable Network Traffic Estimationcited by 6
  6. 2025Deep Probabilistic Modeling of User Behavior for Anomaly Detection via Mixture Density Networkscited by 6
  7. 2025Capsule Network-Based Semantic Intent Modeling for Human-Computer Interactioncited by 5
  8. 2025Collaborative Distillation Strategies for Parameter-Efficient Language Model Deploymentcited by 5
  9. 2025A Data Balancing and Ensemble Learning Approach for Credit Card Fraud Detectioncited by 4
  10. 2025Transferable Modeling Strategies for Low-Resource LLM Tasks: A Prompt and Alignment-Based Approachcited by 4
  11. 2025A Deep Learning Approach to Anomaly Detection in High-Frequency Trading Datacited by 3
  12. 2025Robust Federated Learning with Global Sensitivity Estimation for Financial Risk Managementcited by 1

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Cited by year

Citing papers per year: 2025: 3.

3 papers.

  1. 2025Contrastive Learning-Based Dependency Modeling for Anomaly Detection in Cloud Servicescited by 9
  2. 2025Dynamic Topic Evolution with Temporal Decay and Attention in Large Language Modelscited by 3
  3. 2025Dynamic Anomaly Identification in Accounting Transactions via Multi-Head Self-Attention Networkscited by 1