### Problem Statement  
The rapid evolution of O-RAN has transformed the traditional RAN into a dynamic, software-driven ecosystem. While this architectural innovation enables intelligence, agility, and vendor interoperability, it simultaneously opens the door to new categories of security vulnerabilities. The introduction of programmable RAN components (e.g., xApps and rApps) and ML models introduces critical security and trust challenges that existing RAN architectures were not designed to address. Firstly, O-RAN components communicate across open interfaces and rely on third-party-developed applications, yet there is no fine-grained trust management system capable of continuously validating the integrity and behavior of these components. Once authenticated, xApps are often implicitly trusted, leaving the system vulnerable to insider threats, compromised supply chains, and logic corruption attacks. The absence of runtime attestation or behavior monitoring allows stealthy attackers to embed malicious logic that could subtly influence scheduling, beamforming, or handover policies to degrade the quality of service or exfiltrate sensitive information [11]. Secondly, while FL is a promising technique for training ML models collaboratively without centralizing data, it is not inherently secure. Malicious participants can engage in model-poisoning attacks, degrade the global model, or infer private attributes of other users through membership inference or gradient inversion. Existing FL frameworks rarely enforce robust verification or trust scoring among participants, nor do they offer real-time detection of anomalous contributions [12; 13]. Furthermore, O-RAN deployments lack a unified security framework that incorporates Zero Trust principles. Most current implementations operate on traditional perimeter-based defenses and assume implicit trust once a component has passed initial authentication. This is fundamentally incompatible with the Zero Trust Architecture (ZTA) paradigm,which mandates continuous verification, micro-segmentation, and adaptive access controls. Integrating ZTA into O-RAN is non-trivial due to the decentralized nature of RAN elements and the real-time requirements of RIC functions [15]. Finally, there is a critical lack of comprehensive security frameworks that consider the interplay between FL and ZTA within the context of O-RAN. While both FL and ZTA independently offer benefits, their integration introduces trade-offs between latency, privacy, and trustworthiness. For example, enforcing ZTA policies may introduce overhead in Near-Real-Time loops, and incorporating differential privacy into FL could degrade model accuracy or increase convergence times, impacting the quality of RIC decisions [18]. In summary, the core problem is the lack of a unified, lightweight, and real-time-compatible security framework that enforces Zero Trust while enabling secure and private federated intelligence in O-RAN. Without addressing this gap, future intelligent wireless networks remain exposed to advanced threats that could compromise both service quality and user privacy at scale. These gaps suggest the absence of a unified security framework that can preserve both performance and trustworthiness in AI-enabled O-RAN deployments. Hence, the research question we address is as follows: _How can we secure O-RAN optimization workflows against adversarial ML attacks and unauthorized access while maintaining high performance?_  
To address the above challenges, we propose an integrated framework combining ZTA and privacy-preserving FL. The core idea is to embed trust enforcement within the RIC workflow through continuous identity verification and policy micro-segmentation while using differential privacy (DP) to protect global model updates in the FL process. Our contributions are as follows:  
* We design a novel **threat model** for O-RAN optimization, categorizing vulnerabilities in the RIC/xApp ecosystem using adversarial ML taxonomies [12].