Dynamic Aggregation and Augmentation for Low-Resource Machine Translation Using Federated Fine-Tuning of Pretrained Transformer Models

Machine Translation (MT) for low-resource languages, such as Twi, remains a persistent challenge in natural language processing (NLP) due to the scarcity of extensive parallel datasets. Due to their heavy reliance on high-resource data, traditional methods frequently fall short, underserving low-resource languages. To address this, we propose a fine-tuned T5 model trained using a Cross-Lingual Optimization Framework (CLOF), a unique method that dynamically modifies gradient weights to balance low-resource (Twi) and high-resource (English) datasets. This cross-lingual learning framework leverages the strengths of federated training to improve translation performance while ensuring scalability for other low-resource languages. In order to maximize model input, the study makes use of a carefully selected parallel English-Twi corpus that has been aligned and tokenized. A thorough evaluation of translation quality is provided by the use of SPBLEU, ROUGE (ROUGE-1, ROUGE-2, and ROUGE-L) measures, and Word Error Rate (WER) metrics. A pretrained mT5 model is used to set baseline performance, which acts as a standard for the optimized model. The suggested method shows notable benefits, according to experimental results. The fine-tuned model achieves a remarkable increase in SPBLEU from 2.16% to 71.30%, a rise in ROUGE-1 from 15.23% to 65.24%, and a notable reduction in WER from 183.16% to 68.32%. These findings highlight the effectiveness of CLOF in addressing the challenges of low-resource MT and enhancing the quality of Twi translations. This work demonstrates the potential of combining cross-lingual learning and federated training to advance NLP for underrepresented languages, paving the way for more inclusive and scalable translation systems.