Abstract: Federated learning (FL) enables collaborative model training without sharing raw data, making it well suited for collaborative intelligence among massive heterogeneous terminals in dynamic heterogeneous networks. However, its open participation mechanism makes FL vulnerable to poisoning attacks, where malicious clients can degrade the global model by performing label-flipping, backdoor injection, or model update manipulation. Existing poisoning defense methods primarily rely on robust aggregation strategies to mitigate malicious updates. Nevertheless, under data heterogeneity, these methods often suffer from limited adaptability, inaccurate anomaly detection caused by single-metric evaluation, and the loss of useful information due to hard filtering strategies. To address these issues, a unified poisoning defense framework for federated learning under data heterogeneity is proposed. The framework adopts ConTrust, a multidimensional consistency scoring-based defense method, as the foundation, which identifies malicious updates by jointly evaluating update direction consistency, update magnitude deviation, and principal subspace residuals. For non-independent and identically distributed (Non-IID) scenarios, HistTrust, a history behavior modeling-based defense method, is developed by incorporating local neighborhood references and cross-round reputation information, thereby mitigating the misclassification caused by data heterogeneity and improving malicious client identification. Experiments are conducted on the MNIST, Fashion-MNIST, and CIFAR-10 datasets under multiple poisoning attack settings. Experimental results demonstrate that the proposed defense framework consistently reduces the attack success rate while maintaining high model accuracy under different data distributions, outperforming several representative defense methods in overall performance. The study indicates that designing differentiated defense mechanisms for different data distributions can effectively enhance the robustness of FL against poisoning attacks.