# A Systematic Review on Hybrid AI Models Integrating Machine Learning and Federated Learning
## 3 Background and Related Work
### Hybrid AI Models: A Conceptual Overview
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref.** & **Title/Survey** & **Objective/Summary** & **Hybrid Components** \\ \hline
[61] & A review on advancing cybersecurity frameworks by integrating machine intelligence with Federated Learning & Reviews hybrid cybersecurity architectures combining ML with FL, enhancing detection accuracy while maintaining privacy compliance. & ML + FL \\ \hline
[63] & An innovative multi-agent approach for robust cyber-physical systems using vertical Federated Learning & Introduces a VFL-based reinforcement learning framework using deep Q-networks and actor-critic agents for adversarial modeling in cyber-physical systems. & VFL + reinforcement learning (DQN, A2C agents) \\ \hline
[62] & Performance evaluation of vertical Federated Machine Learning against adversarial threats on wide-area control system & Evaluates VFL (via SplitNN) in grid systems under adversarial machine learning threats such as DoS and data injection attacks. & VFL + SplitNN (DL) \\ \hline
[64] & Federated learning architectures for credit risk assessment: A comparative analysis of vertical, horizontal, and transfer learning approaches & Introduces an adaptive hierarchical hybrid Federated Learning (HHFL) architecture potentially applicable to cybersecurity solutions. & Hierarchical FL (VFL, HFL, transfer learning components) \\ \hline
[66] & An interpretable Federated Learning-based network intrusion detection framework (FEDFOREST)} & Combines gradient boosted decision trees (GBDT) with HFL to build a privacy-enhanced, interpretable, and effective IDS. & HFL + GBDT (ML) \\ \hline
[65] & A Federated Learning-based zero trust IDS for IoT & Uses CNN + BiLSTM under HFL to detect intrusions while maintaining zero-trust data confidentiality. & HFL + CNN (DL) + BiLSTM (DL) \\ \hline
[67] & Federated Deep Learning for collaborative intrusion detection & Introduces an FDL model using local DNNs and a novel Fed+ fusion algorithm for heterogeneous network traffic. & FDL + DNNs (DL) + Fed+ fusion algorithm \\ \hline
[68] & FDL for intrusion detection in consumer-centric IoT & Demonstrates high performance (up to 99.6\%) in detecting multiple IoT attacks while preserving privacy and reducing training time. & FDL (implied DL) \\ \hline
[72] & FMDL: Federated mutual distillation learning & Proposes a defense against backdoor attacks in FL via private model personalization and distillation-based learning. & FL + distillation-based learning (ML) \\ \hline
[73] & Secure FML with flexible topology for privacy-preserving, auditable training. & Proposes a zero-trust, secure FML framework using confidential computing for privacy-preserving, auditable training. & FML + confidential computing \\ \hline
[74] & Swarm-FDL in Internet of Vehicles (IoV) & Merges swarm learning and FDL to reduce overhead and enhance model trustworthiness in IoV systems. & FDL + swarm learning \\ \hline
[75] & Federated Deep Learning for cyber security in the Internet of Things & Compares FDL models (CNN, RNN, DNN) across IoT datasets. & FDL (CNN, RNN, DNN) \\ \hline
[76] & Chained anomaly detection models for Federated Learning & Proposes blockchain-integrated FML for secure anomaly detection. & FML + blockchain \\ \hline
[77] & CYBRIA—Privacy-aware cybersecurity with Federated Learning & Introduces Cyria, a privacy-preserving FL framework with higher accuracy than centralized DNNs. & FL + DNNs \\ \hline
[78] & FLAD: Adaptive Federated Learning for DDoS attack detection & Develops an adaptive FDL approach for dynamic cybersecurity scenarios. & Adaptive FDL (DL) \\ \hline
[79] & GVFL attack study & Shows CNN-based VFL systems are vulnerable to adversarial attacks. & VFL + GNN (DL) \\ \hline
[69] & FEDGAN-IDS: Privacy-preserving IDS using GAN and Federated Learning & Combines GANs and FL to improve IDS in IoT environments. & FL + GAN (DL) \\ \hline
[80] & Fed-ANIDS & Uses autoencoder-based anomaly detection with FedProx, outperforming GAN-based FL models. & FL (FedProx) + autoencoder (DL) \\ \hline \hline \end{tabular}
\end{table}