## 3 Related Work  
In this section, we review key efforts that integrate Zero-Trust Security and FL within the O-RAN RIC/xApp ecosystem for 6G networks. The evolution of 6G wireless networks \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Challenge** & **Impact Metric** & **Benchmark Value** \\ \hline Data Poisoning Attacks & Model Accuracy Degradation & Up to 40\% loss in accuracy [9] \\ \hline Unauthorized Access Attempts & Reduction with Zero-Trust & 89\% reduction compared to conventional baselines [28] \\ \hline Multi-Vendor Risks & Vulnerabilities per Vendor Component & Average 3.2 vulnerabilities per component [28] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Quantitative impact of security challenges in O-RAN [5]. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Requirement** & **Target Metric** & **Benchmark Value** \\ \hline Energy Efficiency & Energy savings (\%) & Up to 32\% with Federated-Learning-based optimization [30, 31] \\ \hline Latency & Response time (ms) & Sub-1 ms for URLLC use cases [29] \\ \hline Trustworthiness & Unauthorized access reduction & 89\% reduction with ZTA [34] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Key 6G network requirements and benchmarks [35]. has intensified the demand for secure, privacy-preserving, and interoperable radio access architectures. The O-RAN (Open Radio Access Network) framework, with its disaggregated and programmable RAN Intelligent Controller (RIC) and extensible xApp ecosystem, emerges as a critical enabler for flexible and scalable network deployments. However, these architectural innovations introduce heightened risks due to third-party application integration and the rapid expansion of the threat surface, especially as AI and ML orchestrate core control functions. To address these risks, the research community has explored complementary paradigms: Zero-Trust Security Architectures, emphasizing dynamic, policy-driven authentication, attestation, and least-privilege micro-segmentation, and FL, enabling distributed, privacy-aware collaborative ML across diverse network entities. Integrating these approaches has been identified as a key requirement for 6G-grade O-RAN RIC/xApp deployments, but comprehensive solutions remain nascent. Several studies directly target zero-trust in O-RAN RIC/xApp environments. For example, ZTRAN introduces a zero-trust microservices framework for xApp authentication, intrusion detection, and secure slicing, with validation on the OAIC research platform, demonstrating improved throughput and fine-grained access control [36]. Similarly, OZTrust and TrustORAN investigate decentralized zero-trust frameworks, leveraging per-packet tagging, distributed tracing, and Blockchain-based dynamic access control to safeguard xApp interactions and containerized workflows in O-RAN testbeds [37; 38]. Parallel research threads address FL deployments within O-RAN but often in isolation from zero-trust controls. Techniques span efficient attack mitigation via federated deep reinforcement learning for jamming detection [39], collaborative model training for RAN slicing [40], federated RL applications deployed as distributed apps (dApps) [41], and robust anomaly detection using hierarchical FL within O-RAN-compliant digital twins [42]. Blockchain-anchored trust and reputation mechanisms for FL contributions have also been articulated, enhancing accountability through smart contracts and on-chain model verification [43]. Some frameworks incorporate cryptographic privacy enhancements, such as differential privacy and secure aggregation [44]. Among the reviewed literature, a small subset explicitly bridges the gap between zero-trust and FL for O-RAN. The ZTORAN architecture exemplifies this convergence by proposing a Blockchain-based decentralized trust management system paired with federated multi-agent RL for continuous threat detection; simulations demonstrate its potential as a secure, resilient framework for 6G O-RAN [45].