With the increasing demand for data privacy and security, Federated Learning (FL) has emerged as a critical distributed machine learning paradigm, safeguarding user privacy by exchanging model parameters between clients and servers rather than raw data. However, existing FL methods face two significant challenges when handling non-independent and identically distributed (Non-IID) data: poor model aggregation across users with varying data distributions and a lack of personalization in locally trained models. To address these issues, we propose a federated meta-learning method based on client gradient clustering and aggregation (GCA-FML), which enhances both model aggregation and personalization. In GCA-FML, clients are grouped according to the similarity of their gradients, thereby reducing interference among users with different data distributions through gradient clustering. Additionally, we employ attention mechanisms to dynamically adjust users’ meta learning rates, facilitating more personalized local models. Our experiments across multiple datasets demonstrate that GCA-FML outperforms other state-of-the-art methods in terms of accuracy and personalization. Notably, in Non-IID environments, GCA-FML significantly improves model convergence speed and personalization performance.