Model Splitting

Model splitting involves partitioning large machine learning models across multiple devices or servers to overcome resource limitations in edge computing and federated learning. Current research focuses on optimizing model partitioning strategies, developing efficient communication and computation resource allocation algorithms (often employing gradient descent variations), and designing novel model architectures to mitigate challenges like stragglers and data heterogeneity. This approach is significant for enabling the deployment of complex AI models on resource-constrained devices, improving training efficiency in collaborative learning settings, and enhancing privacy in distributed training scenarios.