Federated Learning (FL) is a distributed learning technique that maintains data privacy by providing a decentralized training method for machine learning models using distributed big data. This promising Federated Learning approach has also gained popularity in bioinformatics, where the privacy of biomedical data holds immense importance, especially when patient data is involved. Despite the successful implementation of Federated learning in biological sequence analysis, rigorous consideration is still required to improve accuracy in a way that data privacy should not be compromised. Additionally, the optimal integration of federated learning, especially in protein sequence analysis, has not been fully explored. We propose a deep feed-forward neural network-based enhanced federated learning method for protein sequence classification to overcome these challenges. Our method introduces novel enhancements to improve classification accuracy. We introduce dynamic weighted federated learning (DWFL) which is a federated learning-based approach, where local model weights are adjusted using weighted averaging based on their performance metrics. By assigning higher weights to well-performing models, we aim to create a more potent initial global model for the federated learning process, leading to improved accuracy. We conduct experiments using real-world protein sequence datasets to assess the effectiveness of DWFL. The results obtained using our proposed approach demonstrate significant improvements in model accuracy, making federated learning a preferred, more robust, and privacy-preserving approach for collaborative machine-learning tasks.