## 3 Related Work
Nonetheless, most other works approach these domains separately, and comprehensive, empirically validated, integrated ZT + FL frameworks tailored to O-RAN RIC/xApp ecosystems remain at a conceptual or early prototyping stage. This survey summarizes the landscape of integrated and siloed zero-trust and FL solutions for O-RAN in 6G, assessing their architectural depth, empirical validation, and applicability to the unique security and privacy challenges of next-generation networks. Timeline of Significant Developments in Zero-Trust and FL for O-RAN RIC/xApp Ecosystems  
Below is a chronology of key advancements explicitly described in the provided papers, accompanied by observed trends in research focus and methodology. * **Federated DRL for O-RAN Slicing** (Milestone): This is the first application of federated deep reinforcement learning (FL + DRL) to the RAN slicing problem within Open RAN. It proposes a collaborative model training between Mobile Virtual Network Operators for adaptable DRL-based RAN slicing models. Reference: [40]. * _Significance_: This establishes a foundation for federated intelligence in O-RAN but does not include zero-trust elements, focusing solely on collaborative, privacy-aware ML in the RAN environment. * **Prototype Zero Trust Microservices for xApps (ZTRAN)** (Milestone): Implementation of zero-trust security as containerized xApp microservices (authentication, intrusion detection, secure slicing) on the OAIC (Open Artificial Intelligence Cellular) research platform. Empirical analysis is provided on user throughput and latency metrics. Reference: [36]. * _Significance_: Demonstrates the feasibility and impact of microservice-based, zero-trust security directly within O-RAN's nRT-RIC/xApp context but without federated learning. * **Federated RL Apps Framework for O-RAN (OpenAI dApp)** (Milestone): Development of a framework/platform supporting distributed, federated reinforcement learning applications (dApps) as xApps in O-RAN, including evaluation in a test environment. Reference: [41]. * _Significance_: Advances the practical deployment of federated RL for O-RAN optimization and management but does not address zero-trust security integration. * **OZTrust: Zero-Trust Security with Fine-Grained xApp Access Control** (Milestone): Introduction of OZTrust, which enforces fine-grained, zero-trust access control for xApps in O-RAN using novel per-packet policy modules--demonstrated via a prototype system. Reference: [38]. * _Significance_: Delivers concrete, empirical validation of zero-trust policy enforcement at the xApp level, yet remains siloed from FL or ML-layer privacy. _2024_  
* **FL for Jamming Attack Mitigation in O-RAN** (Milestone): Introduction of FL (combined with deep RL) for distributed jamming detection and mitigation in O-RAN, aggregating agent models at the Non-Real-Time RIC. Evaluation is via simulation. Reference: [39]. * _Significance_: Focuses on federated/anomaly-aware intelligence for O-RAN security but not on zero-trust principles or architectural enforcement. * **FL for Anomaly Detection in O-RAN Digital Twin** (Milestone): Development of a hierarchical FL framework for anomaly detection in O-RAN using the Colosseum emulation platform. Reference: [42]. * _Significance_: Advances empirical realism by using an O-RAN-compliant digital twin for security-focused FL validation but without zero-trust orchestration. * **Integrated Zero-Trust + FL Architecture (ZTORAN)** (Milestone): ZTORAN introduces a two-module architecture for O-RAN combining Blockchain-based decentralized trust and dynamic access management, for xApps, and Federated multi-agent RL, for anomaly detection and continuous threat monitoring. Reference: [45]. * _Significance_: First explicit integration of zero-trust and FL for O-RAN RIC/xApps. Simulations highlight the potential of this combined architecture but do not demonstrate deployment on physical or emulated O-RAN testbeds.