Crowdsensing, as a powerful paradigm, enables the collection of large-scale sensor data from distributed participants. However, malicious contributors providing unreliable or tampered data pose significant challenges to data quality and task reliability. This paper proposes a Federated Trust Management (FTM) framework that integrates a trust assessment mechanism into Federated Learning to mitigate the impact of adversarial contributions. The proposed method dynamically adjusts the credibility of participants and introduces a weighted aggregation strategy in FL, thereby reducing the influence of low-quality or malicious data. Experimental results demonstrate that FTM significantly improves global model accuracy, adversarial impact, and enhances the robustness of crowdsensing applications.