The proposed progressive training paradigm can be extended to other machine learning tasks beyond image classification by adapting the concept of dividing the model into blocks and training them sequentially. For natural language processing (NLP), this approach can be applied by dividing the neural network model into blocks based on the layers or components involved in the NLP task, such as tokenization, embedding, recurrent layers, and output layers. Each block can be trained progressively, freezing the parameters of the already trained blocks to reduce memory usage and improve efficiency.
In the case of speech recognition tasks, the progressive training paradigm can be implemented by dividing the model into blocks representing different stages of the speech recognition process, such as feature extraction, acoustic modeling, language modeling, and decoding. By training these blocks sequentially and freezing the parameters of the trained blocks, the memory footprint can be reduced, enabling the participation of memory-constrained devices in the federated learning process for speech recognition.
Overall, the key idea is to adapt the progressive training paradigm to the specific architecture and requirements of the machine learning task at hand, dividing the model into logical blocks and training them progressively to optimize memory usage and improve training efficiency.