With the growing complexity of industrial processes, the scale of production processes tends to be large. The significant amount of measurement data in large-scale processes poses challenges in data collection, management, and storage. In order to perform effective process monitoring in large-scale processes, the distributed process monitoring strategy is widely applied. Meanwhile, product quality is an important indicator for industrial production. Therefore, a novel quality-based distributed process monitoring scheme is proposed. Firstly, the Girvan-Newman (GN) algorithm in complex network divides process variables into multiple sub-blocks. Secondly, greedy algorithm-based high-dimensional mutual information (HDMI) is used to extract quality-related variables in each sub-block, through which the irrelevant and redundant variables are eliminated. Thirdly, the decomposed modified partial least squares (DMPLS) approach is used to detect whether a fault is quality-related or not in each sub-block. Finally, the Bayesian inference strategy is adopted to combine the detection results of all sub-blocks. The effectiveness of the distributed DMPLS approach is illustrated through a numerical simulation and the Tennessee Eastman (TE) process. The results show the superiority of our proposed monitoring scheme.