The techniques developed in CryptoTrain, particularly the CCMul-Precompute and correlation-aware polynomial convolution, can be adapted to support secure training on more complex neural network architectures like transformers and graph neural networks (GNNs) by addressing the unique computational requirements and structures of these models.
Transformers: Transformers rely heavily on attention mechanisms, which involve computing dot products and softmax operations across large matrices. The CCMul-Precompute technique can be extended to precompute the necessary matrix multiplications involved in the attention layers, similar to how it handles linear layers in CryptoTrain. Additionally, the correlation-aware polynomial convolution can be adapted to efficiently handle the multi-head attention mechanism by optimizing the polynomial packing of input embeddings and attention weights, thus reducing the number of polynomial multiplications required during training.
Graph Neural Networks: GNNs operate on graph-structured data, requiring operations like message passing and aggregation. The techniques from CryptoTrain can be utilized to secure the message passing phase by employing homomorphic encryption for the aggregation functions. The correlation-aware polynomial convolution can be tailored to optimize the encoding of node features and their relationships, ensuring that only relevant information is processed, thereby minimizing computational overhead.
Generalization of Techniques: Both techniques can be generalized to accommodate the specific operations of these architectures. For instance, the precomputation phase can be designed to handle various types of operations (e.g., convolutions, attention scores, or message passing) by creating a modular framework that allows for the integration of different cryptographic protocols based on the operation type.
By extending the CryptoTrain framework in this manner, secure training can be achieved for more complex architectures while maintaining efficiency and performance improvements.