FedStitch's innovative block stitching approach can be adapted to various neural network architectures beyond computer vision models by leveraging the modularity of neural networks. This involves partitioning different types of pre-trained models, such as those used in natural language processing (NLP) or speech recognition, into blocks. For instance, transformer models like BERT or GPT can be segmented into blocks corresponding to different layers or attention heads. Each block can then be evaluated for compatibility with local datasets using metrics like Centered Kernel Alignment (CKA), similar to the method used in FedStitch for computer vision models.
Moreover, the hierarchical coordination framework of FedStitch can be applied to recurrent neural networks (RNNs) or long short-term memory (LSTM) networks by treating different time steps or hidden states as blocks. This flexibility allows the framework to stitch together blocks from various architectures, enabling the creation of hybrid models that can leverage the strengths of different neural network types. By utilizing pre-trained models from diverse domains, FedStitch can enhance performance across a broader range of applications, including text classification, sentiment analysis, and audio processing, while maintaining efficiency in resource-constrained environments.