Latency Training

Latency training focuses on optimizing the speed of machine learning model training, particularly in resource-constrained or distributed environments like federated learning. Current research emphasizes techniques like model splitting, layer-wise updates, and dynamic latency adjustment to reduce training time without sacrificing accuracy, often employing transformer-based architectures or modifications to existing models. These advancements are crucial for deploying machine learning in real-time applications, such as speech recognition and recommendation systems, where low latency is paramount for a positive user experience.