# A Systematic Review on Hybrid AI Models Integrating Machine Learning and Federated Learning
## 4 Results and Analysis  
The systematic evaluation of federated machine learning (FML) models, also referred to as FL+ML/DL hybrid models, reveals substantial advantages over standalone machine learning (ML) and federated learning (FL) systems across five key metrics: accuracy, privacy preservation, scalability, explainability, and robustness. As comprehensively illustrated in Table 5, these hybrid approaches effectively balance high-performance capabilities with stringent privacy requirements, directly addressing the inherent limitations of both centralized ML and traditional FL approaches. Their enhanced overall characteristics position them as a compelling solution for addressing the multifaceted challenges of modern cybersecurity applications, particularly in distributed systems and real-time threat detection environments. Accuracy represents a fundamental strength of FML models. While traditional machine learning models typically demonstrate reported accuracy around 85%, federated learning models improve upon this, achieving approximately 90% reported detection rates in various cybersecurity tasks. FML models, leveraging the synergistic integration of centralized learning's precision with decentralized training's adaptability, are reported to achieve the highest accuracy, reaching up to 99.9% on benchmark datasets for specific detection tasks such as intrusion detection and malware classification. This enhanced performance in identifying and mitigating both established and emerging threats underscores the capacity of hybrid models as valuable assets in contemporary cybersecurity strategies [47; 59; 62; 63; 67; 68; 75]. Privacy preservation constitutes a critical advantage of both FL and FML models. Inherently, federated learning enables decentralized model training without sharing raw data, ensuring data remains stored on local devices and significantly reducing risks associated with centralized storage. This architectural approach ensures compliance with regulatory frameworks such as GDPR while maintaining high performance standards. Advanced techniques, including homomorphic encryption, differential privacy, and secure aggregation, are commonly implemented in FML models to safeguard data integrity throughout training and inference processes, making them particularly appropriate for deployment in privacy-sensitive domains [41; 43; 65; 66; 70; 71; 82; 107; 108; 109]. Scalability presents a significant challenge for standalone machine learning systems (often moderate) and traditional federated learning systems (often low-to-moderate), particularly in large-scale, real-time applications. FML models address this constraint by implementing optimized communication protocols, efficient resource allocation strategies, and lightweight architectures. Simulations and experimental studies conducted in IoT networks and distributed systems demonstrate reduced latency and increased throughput, highlighting the operational efficiency of hybrid frameworks. These enhancements make FML models especially suitable for managing the demands of extensive environments, enabling consistent performance in scenarios requiring immediate decision-making and processing [53; 57; 64; 70; 74; 78; 82; 97; 107]. Explainability remains an area requiring substantial improvement across all model types, particularly in hybrid AI frameworks. While interpretability tools such as SHAP (SHapley Additive Explanations) and LIME (Local Interpretable Model-Agnostic Explanations) are employed to enhance transparency in both FL and hybrid models, their integration into complex FML architectures remains limited. This limitation impacts transparency and usability, particularly in high-stakes decision-making contexts such as intrusion prevention and fraud detection, where understanding the rationale behind system decisions is essential.