Model Update

Model update research focuses on improving the efficiency and robustness of updating machine learning models, addressing challenges like communication overhead in federated learning and maintaining compatibility across model versions. Current efforts explore techniques such as binarization of updates, layer-wise updates, and selective update strategies to enhance speed and reduce resource consumption, while also developing methods to ensure compatibility with previous model versions and robustness to data shifts. These advancements are crucial for deploying and maintaining machine learning systems in resource-constrained environments and for ensuring the reliable and consistent performance of models over time in various applications.