Federated learning (FL) is a privacy-preserving distributed learning paradigm in which clients perform localized training iterations and send model updates to a central server rather than sharing raw data. However, data heterogeneity among clients significantly impacts the performance of the global model. Existing FL methods tackle this issue by optimizing the loss function through client-side training, but fail to account for the distinct distribution differences of client data and rely on uniform client weighting during server-side aggregation. This limitation restricts the model’s effectiveness and weakens its generalization ability. To address this, we propose a novel FL optimization method, Clustering FL with Bhattacharyya Distance (CFLBD). This approach integrates the local similarity assessment based on client data heterogeneity and adaptive clustering to dynamically weigh the aggregation of local models in each communication round. Specifically, CFLBD quantifies the distribution similarity by calculating the Bhattacharyya distance between the local and global models. Then, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is leveraged to adaptively cluster the local models on the server, minimizing the distribution variance within each cluster. The global aggregation relies on a dynamically weighted average of the clustered clients, enhancing its performance and robustness across heterogeneous data scenarios. Experimental results show that CFLBD consistently outperforms baseline methods in accuracy and robustness across various heterogeneity settings.