# A Systematic Review on Hybrid AI Models Integrating Machine Learning and Federated Learning
## 6 Conclusions  
This systematic review underscores that federated machine learning (FML) models offer a compelling and balanced solution to the multifaceted challenges of modern cybersecurity. By synergistically integrating the strengths of machine learning (ML)/Deep Learning (DL) with federated learning (FL), these hybrid models significantly advance beyond traditional standalone approaches. The comprehensive analysis reveals their demonstrated superiority in achieving high detection accuracy, ensuring robust privacy preservation, and enhancing resilience against adversarial threats. FML models also show promising advancements in scalability, making them increasingly suitable for distributed and real-time cybersecurity applications.  
Despite these considerable strides, the review identifies key areas requiring further research. Optimizing scalability for large-scale and highly heterogeneous networks remains a critical challenge, as does reducing communication overhead. Furthermore, improving explainability in complex FML architectures is essential to foster trust and facilitate high-stakes decision-making. Emerging trends, such as quantum-enhanced FL, neuro-symbolic AI, agentic hybrid AI, and dynamic retraining firewalls, offer promising avenues to address these limitations.  
Ultimately, by rigorously defining the landscape of FML models and highlighting specific research gaps and emerging solutions, this review provides a foundational understanding for future empirical and theoretical work. Addressing the identified challenges and capitalizing on the inherent strengths of hybrid AI systems will be pivotal in translating the promise of FML into widely adopted, robust, scalable, and transparent cybersecurity solutions capable of combating the ever-evolving threat landscape.  
Conceptualization, J.-E.M., M.K. and Y.M.; methodology, J.-E.M., M.K., Y.M.; software, J.-E.M.; validation, Y.M. and K.E.G.; formal analysis, J.-E.M.; investigation, J.-E.M.; resources, J.-E.M.; data curation, J.-E.M. and M.K.; writing--original draft preparation, J.-E.M. and Y.M.; writing--review and editing, J.-E.M., M.K., Y.M. and K.E.G.; visualization, J.-E.M.; supervision, Y.M.; project administration, Y.M.; funding acquisition, Y.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding. The APC was funded by the authors.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article as it is based on a systematic review of previously published literature. All referenced sources are publicly available and cited within the manuscript.  
The authors would like to acknowledge the support of the LaSTI Laboratory at ENSA Khouribga, Sultan Moulay Slimane University, for providing access to academic resources and guidance throughout the research process. We also thank our colleagues for their constructive feedback during the manuscript revision phase.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
A2C Actor-Critic AI Artificial Intelligence BiLSTM Bidirectional Long Short-Term Memory CNN Convolutional Neural Network DDoS Distributed Denial-of-Service DL Deep Learning DQN Deep Q-Learning GAN Generative Adversarial Network GNN Graph Neural Network GDPR General Data Protection Regulation HAI Hybrid AI