I’m working on a federated learning project using PyTorch, focusing on medical imaging (MRI) data. Despite using an SSD, the dataset loading phase is unusually slow, and the GPU utilization remains very low across global epochs. Each client handles a significant volume of data, and the frequent client switching seems to exacerbate the issue. I’m looking for ways to optimize data loading and enhance GPU utilization.