Federated Learning Approaches for Cross-Institutional Threat Detection Without Data Sharing Collaborative ML models for Financial Cybersecurity that Preserve Privacy

SecureFedShield: An Adaptive Privacy-Preserving Federated Defense Framework Against Adversarial Attacks in Financial Fraud Detection

SecureFedShield is proposed, a privacy-preserving federated learning framework designed for secure financial fraud detection in adversarial environments that integrates adaptive privacy protection, trust-aware client evaluation, adversarial update detection, and robust model aggregation into a unified architecture.

Adversarial Machine Unlearning for Privacy-Preserving Threat Intelligence Sharing

A novel framework for adversarial machine unlearning is introduced to enable privacy-preserving threat intelligence sharing and lays the foundation for secure and compliant knowledge transfer in federated security operations and collaborative defence ecosystems.

Trustworthy and privacy-preserving decentralized federated learning with multi-layer defense for secure collaborative AI

Decentralized Federated Learning (DFL) enables collaborative artificial intelligence model training without centralizing sensitive data, making it suitable for privacy-critical and distributed intelligent systems such as healthcare, Industrial IoT, and smart digital infrastructure. Despite its advantages, DFL remains vulnerable to privacy leakage through shared model updates and to model poisoning and backdoor attacks that compromise system reliability, robustness, and trustworthiness. Existing defense mechanisms primarily address either privacy preservation or poisoning robustness independently and often exhibit limited effectiveness under adaptive or high-ratio adversarial settings. This work proposes a trustworthy and privacy-preserving decentralized federated learning framework that jointly addresses these challenges through two integrated components: (i) a hybrid privacy mechanism based on public dataset pretraining followed by differentially private fine-tuning, and (ii) a multi-layer model defense architecture designed to mitigate poisoning and backdoor attacks across decentralized peer-to-peer environments. The framework integrates local data sanitization, peer-side model verification, robust trimmed-mean aggregation, and runtime inference protection to provide defense-in-depth across both training-time and inference-time attack surfaces. An adversary model and operational assumptions are formally defined, and the framework is evaluated under strong adversarial conditions, including a 20% poisoning ratio. Experimental results demonstrate consistent robustness improvements over a vanilla DFL baseline. While the baseline model achieves a clean accuracy of 83.10%, the proposed framework improves clean performance to 86.12%. Under adversarial conditions, accuracy improves from 37.71% to 53.88% for Fast Gradient Sign Method (FGSM) attacks, from 21.75% to 46.40% for Projected Gradient Descent (PGD) attacks, and from 40.62% to 67.35% for Carlini–Wagner (CW) attacks. For backdoor-based poisoning attacks such as BadNets and Blended attacks, the defense pipeline restores model accuracy to above 86% while maintaining stable benign performance. These findings demonstrate that the proposed framework provides an effective balance between privacy preservation, adversarial robustness, and trustworthy decentralized collaborative learning for secure AI-driven systems.

A privacy preserving adaptive cybersecurity framework for defending fintech ecosystems against advanced persistent threats

Federated Learning Privacy Protection Collaboration Model Based on Improved Cyber Threat Collaborative Analysis Capability

Traditional federated learning has challenges such as slow model convergence, lagging situational awareness, and risk of gradient privacy leakage in cyber threat collaborative analysis capability. This study proposes a federated learning privacy-preserving collaboration model based on threat intelligence drive and hierarchical aggregation. The model optimizes participating nodes through a dynamic client selection mechanism, uses a hierarchical aggregation strategy to balance the learning of basic features and advanced threat patterns, and introduces an adaptive differential privacy mechanism to strengthen gradient protection. The experiment is based on the CIC-IDS-2018 public dataset, which covers various types of attacks in real network environments, with a data volume of approximately 5 million pieces. It is divided into 50 clients in a non-independent and identically distributed manner to simulate cross organizational collaboration scenarios. In the threat detection task, the model designed by the research institute achieved an accuracy of 91.8% and an F1 Score of 89.8% compared to baseline models such as FedAvg, FedProx, and DP FedAvg. All indicators were superior to the comparison model. In addition, in the advanced persistent threat attack scenario, the attack chain detection rate increased to 95.6%, and the average detection time was shortened to 2.8 hours. In terms of privacy protection, the Rényi privacy loss was only 2.89 with a budget of ε=3.0. The proposed model effectively improves the efficiency and timeliness of collaborative detection of cross-organizational threats while ensuring data privacy and provides a feasible solution for building a safe and reliable collaborative defense system. It should be pointed out that while improving detection performance, the model introduces additional communication overhead caused by hierarchical aggregation and dynamic selection mechanisms. The average communication traffic in the experiment was about 13.8 GB. As the number of clients expands to a larger scale, the computational load and scheduling complexity of the coordination layer will further increase. In addition, although the non-independent and identically distributed data partitioning used in the experiment can simulate real heterogeneous scenarios, the convergence efficiency of the model under extreme distributions still needs further verification. The above limitations will be optimized in future work by introducing asynchronous aggregation and lightweight communication protocols.

SecShield: A Privacy-Preserved Federated Learning Model to Detect Zero-Day Malware Attack

The proliferation of Internet of Things (IoT) devices has amplified the attack surface for large-scale cyber threats, with rapidly evolving malware families such as Mirai posing significant detection challenges. Existing side-channel and host-based approaches are limited by poor generalization to unseen variants and the absence of robust privacy protections during training. In this paper, we propose SecShield, a federated deep learning framework for privacy-preserving malware detection in resource-constrained IoT environments. SecShield employs a three-party secure computation model, where clients locally train a shared deep neural network on power side-channel traces and transmit only noisy parameter updates to a central server via an aggregator, preventing raw data exposure. Differential privacy is incorporated by injecting calibrated noise into gradients, mitigating inference risks such as data reconstruction and poisoning attacks. This decentralized architecture enables continuous adaptation to zero-day malware, reduces communication overhead, and ensures robustness against adversarial manipulation. Experimental evaluations on IoT power trace datasets demonstrate that SecShield achieves high detection accuracy over 98% while preserving client data confidentiality and maintaining computational efficiency.