  O-RAN specifications reshape RANs with function disaggregation and open
interfaces, driven by RAN Intelligent Controllers. This enables data-driven
management through AI/ML but poses trust challenges due to human operators'
limited understanding of AI/ML decision-making. Balancing resource provisioning
and avoiding overprovisioning and underprovisioning is critical, especially
among the multiple virtualized base station(vBS) instances. Thus, we propose a
novel Federated Machine Reasoning (FLMR) framework, a neurosymbolic method for
federated reasoning, learning, and querying. FLMR optimizes CPU demand
prediction based on contextual information and vBS configuration using local
monitoring data from virtual base stations (vBS) on a shared O-Cloud
platform.This optimization is critical, as insufficient computing resources can
result in synchronization loss and significantly reduce network throughput. In
the telecom domain, particularly in the virtual Radio Access Network (vRAN)
sector, predicting and managing the CPU load of vBSs poses a significant
challenge for network operators. Our proposed FLMR framework ensures
transparency and human understanding in AI/ML decisions and addresses the
evolving demands of the 6G O-RAN landscape, where reliability and performance
are paramount. Furthermore, we performed a comparative analysis using
\textit{DeepCog} as the baseline method. The outcomes highlight how our
proposed approach outperforms the baseline and strikes a better balance between
resource overprovisioning and underprovisioning. Our method notably lowers both
provisioning relative to the baseline by a factor of 6.
