With the proliferation of massive data and the ever-growing demand for intelligent applications, ensuring data security has become a critical measure for enhancing data quality and realizing data value. The cloud-edge-client architecture has emerged as a promising technology for efficient data processing and optimization. Federated learning (FL), an efficient decentralized machine learning paradigm that can provide privacy protection for data, has garnered extensive attention from academia and industry in recent years. However, FL has demonstrated inherent vulnerabilities that render it highly susceptible to poisoning attacks. Most existing methods for defending against poisoning attacks rely on continuously updated space, but in practical scenarios, those methods may be less robust when facing flexible attack strategies and varied attack scenarios. Therefore, this study proposes FedDiscrete, a defense method for resisting poisoning attacks in cloud-edge FL (CEFL) systems. The key idea is to compute local rankings on the client side using the scores of network model edges to create discrete update space. To ensure fairness among clients participating in the FL task, this study also introduces a contribution metric. In this way, FedDiscrete can penalize potential attackers by allocating updated global rankings. Extensive experiments demonstrate that the proposed method exhibits significant advantages and robustness against poisoning attacks, and is applicable to both independent and identically distributed (IID) and non-IID scenarios, providing protection for CEFL systems.