Optimal Batch

Optimal batch size determination in machine learning focuses on finding the ideal number of data samples processed before updating model parameters, balancing computational efficiency with generalization performance. Current research investigates adaptive batch size strategies, often employing reinforcement learning or continuous-time control methods to dynamically adjust batch sizes during training, particularly within distributed and federated learning settings. These efforts aim to improve training speed and model accuracy across various architectures and algorithms, impacting the efficiency and scalability of machine learning applications. The optimal batch size is also shown to be influenced by factors such as data heterogeneity, the presence of Byzantine failures, and the specific learning algorithm used.