Federated Learning (FL) offers a privacy-preserving approach to distributed machine learning by enabling collaborative training across multiple clients. Existing research mainly focuses on client selection to improve global model convergence, addressing client resource heterogeneity. However, these methods often separate efficiency from security, neglecting the protection of local model parameter interactions and risking client privacy. To solve this, we propose a framework that balances optimal client selection with secure parameter interactions, protecting client models during training and ensuring correct aggregation without privacy leaks. To prevent man-in-the-middle attacks, a BLS signature mechanism ensures data integrity. Additionally, a Deep Reinforcement Learning (DRL)-based self-weighting method mitigates masking effects on server aggregation, enabling accurate weighted aggregation without masks. Experiments show our framework significantly improves model accuracy over traditional FL aggregation methods.