###### Abstract  
The Open Radio Access Network (O-RAN) paradigm promises unprecedented flexibility and cost efficiency for 6G networks but introduces critical security risks due to its disaggregated, AI-driven architecture. This paper proposes a secure optimization framework integrating zero-trust principles and privacy-preserving Federated Learning (FL) to address vulnerabilities in O-RAN's RAN Intelligent Controllers (RICs) and xApps/rApps. We first establish a novel threat model targeting O-RAN's optimization processes, highlighting risks such as adversarial Machine Learning (ML) attacks on resource allocation models and compromised third-party applications. To mitigate these, we design a Zero-Trust Architecture (ZTA) enforcing continuous authentication and micro-segmentation for RIC components, coupled with an FL framework that enables collaborative ML training across operators without exposing raw network data. A differential privacy mechanism is applied to global model updates to prevent inference attacks. We validate our framework using the DAWN Dataset (5G/6G traffic traces with slicing configurations) and the Open-RAN Gym Dataset (O-RAN-compliant resource utilization metrics) to simulate energy efficiency optimization under adversarial conditions. A dynamic DU sleep scheduling case study demonstrates 32% energy savings with <5% latency degradation, even when data poisoning attacks compromise 15% of the FL participants. Comparative analysis shows that our ZTA reduces unauthorized RIC access attempts by 89% compared to conventional O-RAN security baselines. This work bridges the gap between performance optimization and trustworthiness in next-generation O-RAN, offering actionable insights for 6G standardization. O-RAN architecture; Zero-Trust Architecture (ZTA); Federated Learning (FL); 6G; energy efficiency optimization; adversarial Machine Learning (ML); +
Footnote †: journal:  
O-RAN promotes openness and interoperability by disaggregating traditional, vertically integrated RAN components into modular and standardized functions, including the Central Unit (CU), Distributed Unit (DU), and Radio Unit (RU) [4]. These are managed via open interfaces and orchestrated by a software-defined RAN Intelligent Controller (RIC), which can dynamically optimize network behavior using AI and ML models [5]. The RIC is separated into two planes: the Near-Real-Time (near-RT RIC) and the Non-Real-Time RIC (non-RT RIC) [6]. The near-RT RIC hosts microservices known as xApps that perform closed-loop control tasks such as scheduling, handover management, interference mitigation, and beam management with millisecond-level responsiveness. The non-RT RIC supports rApps that handle policy orchestration, analytics, and the training of ML models over longer timescales [7]. A key enabler of this intelligent control is the integration of ML. ML algorithms can analyze vast volumes of contextual and performance data to derive insights and predictions that guide real-time decision-making. To protect user privacy and reduce the need for centralized data storage, FL has been introduced into the RIC workflow. FL allows distributed RIC nodes to collaboratively train models without sharing raw data, thus aligning with both privacy regulations and efficiency goals [8]. However, the same openness and programmability that empower O-RAN also introduce significant risks. For instance, the O-RAN architecture's reliance on open interfaces and third-party-developed applications increases the attack surface, making it vulnerable to exploitation. A recent study by Liyanage et al. (2023) highlights several security challenges in O-RAN, including risks associated with untrusted third-party xApps and potential vulnerabilities in open interfaces [9]. Similarly, a report by Nokia Bell Labs emphasizes the need for advanced security measures in O-RAN deployments, particularly due to the increased attack surface introduced by vendor interoperability and network softwarization [10].