## 6 Conclusions  
In this research, we demonstrated the effectiveness of fine-tuning a pretrained multilingual model (mT5) for low-resource language translation, utilizing cross-lingual learning and a federated-like approach. The fine-tuned model exhibited significant improvements in translation quality, outperforming the baseline model across all key metrics, including SpBLEU, ROUGE, and WER. Our approach successfully bridged the gap between high-resource and low-resource languages, substantially enhancing the translation accuracy and fluency of Twi, a language with limited NLP resources. This refined model has a broad range of potential applications. It can be integrated into real-time translation systems to facilitate communication between English and Twi speakers. Additionally, it holds promise for language learning tools, assisting in both learning and teaching Twi. The approach can also be applied to social media and communication platforms, helping Twi speakers overcome language barriers and increase their digital accessibility. The improved translation of Twi is especially important as it enhances NLP capabilities for a language that has historically been underrepresented in machine translation. This opens up new possibilities for integrating Twi into digital platforms, improving communication, and enhancing content generation for the large population of native speakers. Despite the promising results, there are areas that warrant further improvement. Expanding the dataset with larger and more diverse corpora would enhance the model's ability to generalize across different linguistic patterns. Additionally, leveraging newer pretrained models, such as mT5-Base or larger variants trained on more extensive multilingual corpora, could further refine translation performance. Future work should focus on improving accuracy for rare phrases by expanding the dataset, implementing subword tokenization, and exploring data augmentation techniques like backtranslation. Combining synthetic data generation with real-world corpora would enhance the model's adaptability, especially for colloquial expressions and rare linguistic structures. Furthermore, adapter-basedfine-tuning and prompt tuning offer promising avenues for efficiently updating the model, enabling scalability and resource-efficient adaptation to other low-resource languages. While our model represents a significant advancement in Twi translation, challenges such as overfitting due to limited data and adapting federated-like training to diverse linguistic structures remain. The model occasionally exhibits rigidity in sentence structures, struggles with highly inflected words, and sometimes over-prioritizes high-resource languages. To mitigate these issues, strategies such as data augmentation, regularization techniques, subword tokenization, and adaptive gradient weighting should be explored. These methods would help improve the model's ability to better capture the linguistic nuances of Twi. We also suggest incorporating additional techniques, such as adapter-based learning, LoRA, and multilingual fine-tuning baselines like mBART, to further improve translation performance and generalization across multiple languages. These techniques are expected to enable more efficient updates when incorporating new languages or domains, ensuring better scalability in low-resource environments. The future of low-resource language translation relies not only on individual research efforts but also on collective contributions from the broader NLP community. Open-source, multilingual datasets and tools are essential for advancing machine translation models and fostering a more inclusive digital ecosystem. By fostering collaborations between academic researchers, industry leaders, and language communities, we can accelerate the development of open-access resources, ensuring sustainable progress in machine translation for underrepresented languages. Conceptualization, E.A. and V.A.O. ; methodology, E.A. ; software, E.A. ; validation, E.A., V.A.O. and A.B.Q. ; formal analysis, E.A.