To address the degradation of model performance caused by statistical heterogeneity under Non-Independent and Identically Distributed （Non-IID） data in federated learning， a Federated learning framework integrating Dynamic feature alignment and Temperature-aware Aggregation （FedDTA） was proposed. In the framework， client drifts were mitigated through dynamic feature alignment and temperature-aware aggregation collaboratively. It has two core components： a dynamic regularization approach based on Sliced Wasserstein Distance （SWD） was used to achieve local-global feature distribution alignment via low-dimensional Monte Carlo projections， thereby reducing computational complexity and suppressing feature drifts； a hierarchical aggregation strategy incorporating a learnable projection network with annealing temperature scheduling was used to allocate client weights dynamically according to parameter differences. Experimental results indicate that under strong heterogeneity （Dirichlet α=0.1） condition， in accuracy， FedDTA outperforms suboptimal FedKTL（Federated Knowledge-Transfer-Loop） and FedCMD （Federated learning with Contrastive cloud-edge Model Decoupling） by 1.698 and 0.714 percentage points on the CIFAR-10 and CIFAR-100 datasets， respectively， demonstrating superior generalization capability in multi-data scenarios. Ablation experimental results confirm that SWD alignment reduces feature drifts significantly， while temperature scheduling optimization balances the exploration with exploitation. Without exposing raw data， FedDTA provides theoretical and methodological supports for privacy-sensitive scenarios such as medical collaboration and the industrial Internet of Things.