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Privacy-preserving Federated Deep Learning For Robust Anomaly Detection In Distributed Security Sensing Systems.

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Privacy-preserving Federated Deep Learning For Robust Anomaly Detection In Distributed Security Sensing Systems.

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Privacy-Preserving Federated Deep Learning for Robust Anomaly Detection in Distributed Security Sensing Systems.

Di Xu^1,2, Hongli Chen^2,3, Yansen Zeng^1,2

¹China Agricultural University, Beijing 100083, China.

Sensors (Basel, Switzerland)

|June 26, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

A new federated deep anomaly detection framework enhances financial security by enabling collaborative modeling without sharing sensitive data. This approach ensures robust monitoring of transactions and system anomalies in distributed environments.

Keywords:

artificial intelligence-driven sensing distributed security sensing edge intelligence intelligent IoT security non-IID data

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Area of Science:

Financial Technology (FinTech)
Cybersecurity
Artificial Intelligence (AI)
Machine Learning (ML)

Background:

Financial security sensing data is heterogeneous, dynamic, and privacy-sensitive.
Traditional centralized anomaly detection fails in distributed systems requiring cross-institutional collaboration and privacy protection.
Need for robust monitoring of transaction and system anomalies in financial AI security.

Purpose of the Study:

To propose a data-local federated deep anomaly detection framework for distributed financial security sensing systems.
To enable cross-node collaborative modeling while protecting client data privacy.
To achieve robust monitoring of financial transaction and system anomalies.

Main Methods:

Constructed a local deep financial security sensing representation module for temporal encoding and attention-based modeling.
Developed a data-local federated optimization and personalized aggregation mechanism for cross-node knowledge sharing without raw data transmission.
Employed local personalized detection heads for non-IID financial data and an adversarially robust security detection strategy.

Main Results:

Achieved high performance in financial anomaly detection: 92.37% Accuracy, 89.41% Precision, 88.26% Recall, 88.83% F1-score, 93.06% AUC, and 89.15% PR-AUC.
Significantly outperformed baseline methods including Isolation Forest, Autoencoder, LSTM, Transformer, FedAvg, FedProx, SCAFFOLD, and MOON.
Demonstrated robustness against various perturbations and malicious client scenarios, maintaining high F1-scores.

Conclusions:

The proposed framework effectively addresses challenges of data localization, node heterogeneity, and attack perturbations in financial AI security.
Local representation learning, personalized federated optimization, adversarial training, and trust-aware aggregation are crucial components.
Provides an efficient intelligent anomaly detection solution for financial AI security monitoring.