This paper investigates the alarm response issue for attack detection in nonlinear cyber-physical systems under false data injection attacks. A fuzzy model is developed to address the nonlinear characteristics of cyber-physical systems. For the first time, a distributed fusion strategy is introduced in the fuzzy model to detect false data injection attacks, enabling it to handle more complex real-world scenarios and improve detection accuracy and reliability, thereby enhancing alarm response speed. Next, to achieve real-time online anomaly detection, deployed sensors transmit data to a monitoring center through a communication network. Considering bandwidth limitations, multiple finite-level uniform quantizers are employed to reduce data packet size, thereby improving transmission efficiency. Then, an optimal distributed fusion scheme is designed using convex optimization to enhance detection accuracy in the presence of quantization errors. Finally, using a mass-spring-damping system as an example, it is demonstrated that the proposed method responds to attacks more rapidly compared to a single-sensor system, showing significant advantages.