In this study, we introduce a decentralized architecture aimed at enhancing the efficiency of precise point positioning real-time kinematics (PPP-RTK) in large-scale networks with a shared pivot station. Initially, we partitioned the extensive network into multiple smaller subnetworks (SNs), each with a common pivot station. Subsequently, we computed augmentation parameters for each SN by applying precise orbit corrections and ionosphere-weighted constraints. However, applying the estimated augmentation parameters directly to users across subnetworks encounters challenges due to inter-SN discontinuities. To address this issue, we made an assumption regarding the common pivot station, enabling the formulation’s ability to integrate parameters across SNs. Consequently, the estimated parameters from each SN are integrated and broadcast to users. This architecture offers a reduced computational burden than the centralized PPP-RTK architecture, which handles a full-scale network simultaneously. Moreover, the integration process effectively removed the discontinuity of augmentation parameters between SNs, enabling seamless user positioning even when transitioning between SNs. To evaluate the effectiveness of our proposed architecture, we gathered dual-frequency global positioning system (GPS) observation data from over 40 continuously observed reference stations (CORS) in Korea. These data were then partitioned into four SNs, each sharing a common pivot station. Subsequently, we compared the static positioning error and processing time of our proposed architecture with those of the centralized architecture.