Split and Fit

"Split and Fit" encompasses a range of techniques that partition data, models, or computational tasks to improve efficiency, privacy, or robustness in machine learning. Current research focuses on applying this principle to diverse areas, including federated learning (SplitFed), reinforcement learning (SAPG), and neural network training (SplitVAEs, SPLITZ), often employing variational autoencoders, message-passing neural networks, or splitting algorithms tailored to specific model architectures. These methods address challenges in large-scale distributed systems, privacy-preserving computation, and improving the generalization and robustness of models, impacting fields from healthcare and finance to environmental modeling.