Enhancing Software Defect Prediction Security via Federated BLSTM with Differential Privacy

2026-06-01T09:53:27+08:00

Software Defect Prediction (SDP) is an issue of paramount importance for improvement of software quality but data isolation and privacy concerns across organizations affects efficacy of SDP. By making use of a Federated Learning (FL) framework in combination with Differential Privacy (DP) for collective learning without exchanging any raw data, we provide a unique solution to this problem. The study makes use of advanced deep learning architectures the baseline Neural Network (NN), the sequential Long Short-Term Memory (LSTM) and the contextual Bidirectional LSTM (BLSTM) that were trained on the ten different, non-IID client datasets (software projects). Data processing included logarithmic transformation, Min Max Scaling, while the Synthetic Minority Over-sampling Technique (SMOTE) algorithm was used to address local class imbalance. The application of DP noise ensured the privacy of the model updates by quantifying them before aggregation using FedAvg. The results are a definite success for recurrent models with the BLSTM network coming on top, achieving a peak Global Accuracy of 99.05% and an F1-Score of 98.9% with the constraint of FL-DP. In comparison, the centralized, non-private BLSTM had slightly better performance in terms of accuracy, 99.3%, proving that the FL-DP framework is able to preserve close to optimal performance while increasing data security drastically. These results confirm that FL-DP is a powerful, safe and open-source paradigm for collaborative SDP, which enables a base solution for organizations that need to achieve high predictive power while maintaining high data confidentiality.

An Intelligent Clinical Decision Support Framework for Heart Disease Risk Prediction

2026-05-21T14:46:08+08:00

Cardiovascular diseases (CVDs) are among the leading causes of mortality worldwide. Early detection and risk stratification are critical for preventive care. Traditional machine learning (ML) models can predict heart disease risk but often lack interpretability and fail to integrate with real-time clinical data. Recent advances in fine-tuned large language models (Custom GPT) offer natural language explanations but are limited by insufficient interoperability with heterogeneous healthcare data sources. This study aims to design and evaluate a Model Context Protocol (MCP)–enabled Custom GPT framework that integrates ML-based predictive models with external healthcare systems—including EHRs, laboratory APIs, and wearable devices—to deliver context-aware, explainable, and clinically actionable heart disease risk predictions. The experimental evaluation was conducted on a validated cardiovascular dataset containing 303 patient records and 14 clinically relevant attributes derived from publicly available clinical repositories. Experimental evaluation demonstrated improved predictive accuracy (approximately 88% with the XGBoost ensemble) and robustness compared to standalone models. MCP integration enabled dynamic contextual awareness, reduced latency in tool orchestration, and enriched interpretability through RAG-based explanations. Clinician and patient evaluations confirmed enhanced usability and transparency. This approach paves the way for broader adoption of agentic AI in clinical workflows.

Journal of Electronic & Information Systems

Enhancing Software Defect Prediction Security via Federated BLSTM with Differential Privacy

An Intelligent Clinical Decision Support Framework for Heart Disease Risk Prediction